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"n_filas_con_objetivo": 1484, "n_filas_leidas": 1484, "n_test": 298, "n_predictores": 8 }, "pc3": { "plan_digest": "45bdb346dffe36b7000269e93d4691fd33afc585c043995bea04deea1a6ad592", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 1563, "n_filas_leidas": 1563, "n_test": 313, "n_predictores": 37 }, "us_crime": { "plan_digest": "ca3c04979283f93c3b59d60758fb0230976913fc69e47fdb0efaeac30d31a6ad", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 1994, "n_filas_leidas": 1994, "n_test": 399, "n_predictores": 126 }, "mfeat-factors": { "plan_digest": "b6ddc3577071b3033880c245d74283f280e5a351fa88f45ae6684167e7372058", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 2000, "n_filas_leidas": 2000, "n_test": 400, "n_predictores": 216 }, "ozone-level-8hr": { "plan_digest": "08311f33d71562f9a9d417b39852247f444d7e5b1e89ff0f333865f98d72f439", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 2534, "n_filas_leidas": 2534, "n_test": 507, "n_predictores": 72 }, "splice": { "plan_digest": "7b4c0c0e8a994564a4817159265052be08e393743b3997cec8369907a3799af9", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 3190, "n_filas_leidas": 3190, "n_test": 638, "n_predictores": 60 }, "kr-vs-kp": { "plan_digest": "e7f4547846217e12abd89c23b83759e862a470c113acc1e5cd371f1968a55f28", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 3196, "n_filas_leidas": 3196, "n_test": 639, "n_predictores": 36 }, "Internet-Advertisements": { "plan_digest": "f56f96b91e258d2a556e53ebf9aeb815bce3cd5fd5ee559071ae9497e9b1ea4f", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 3279, "n_filas_leidas": 3279, "n_test": 656, "n_predictores": 1558 }, "sick": { "plan_digest": "eb3614751e36a01d80b57d9701a6ee26c809f4c006f8e9f943af3746ea0299a7", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 3772, "n_filas_leidas": 3772, "n_test": 754, "n_predictores": 29 }, "SAT11-HAND-runtime-regression": { "plan_digest": "d4e8eadcbb2dfeccf7884b7d62c1e62af7bf535aed1c5bfcc093b9f0d0ea6c4f", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 4440, "n_filas_leidas": 4440, "n_test": 888, "n_predictores": 116 }, "wilt": { "plan_digest": "0c6966138aa733275b8967a246938d89e98cf206cd90e5e078a1e57c950e5e09", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 4839, "n_filas_leidas": 4839, "n_test": 968, "n_predictores": 5 }, "Satellite": { "plan_digest": "1e4cc906ae0a83c9c837d8e63fb33a23c56f7588301affd4a71a9fa8b5df6b14", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 5100, "n_filas_leidas": 5100, "n_test": 1020, "n_predictores": 36 }, "optdigits": { "plan_digest": "4b33a2c4743cfc59b630cd6687f79c8c303fb686cac54e6c93043071554e8a98", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 5620, "n_filas_leidas": 5620, "n_test": 1124, "n_predictores": 64 }, "wine_quality": { "plan_digest": "fe76feaff18816a77d8978ec9957f0ddcc65d905f88f8336f12d639dce9eb107", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 6497, "n_filas_leidas": 6497, "n_test": 1299, "n_predictores": 11 }, "jm1": { "plan_digest": "483d31b32e5d296cb9157784951ce916385b23c7acf21f75d43da9a69af00bd3", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 10885, "n_filas_leidas": 10885, "n_test": 2177, "n_predictores": 21 }, "pendigits": { "plan_digest": "85f9dfb58797a389e50f89951229b463b9ccf0e8928ea895e2e21dfa79adbd6c", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 10992, "n_filas_leidas": 10992, "n_test": 2199, "n_predictores": 16 }, "PhishingWebsites": { "plan_digest": "00ace3a7c2d2102269404e165d9eda59c775f36d2c66ac4086bd308e398f4ed7", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 11055, "n_filas_leidas": 11055, "n_test": 2211, "n_predictores": 30 }, "pol": { "plan_digest": "477a7daee19c17b5f1625bcd060e1233c83f6f8bb3e4bf5b8e89bb6f1480785d", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 15000, "n_filas_leidas": 15000, "n_test": 3000, "n_predictores": 48 }, "letter": { "plan_digest": "c607378c91a7ed58585d3e0143316a716689861945bed8898b5b42e4127c5cdf", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 3, "folds_obtenidos": 5, "n_pliegues_pedidos": 15, "n_pliegues_obtenidos": 15, "limites": [], "n_filas_con_objetivo": 20000, "n_filas_leidas": 20000, "n_test": 4002, "n_predictores": 16 }, "house_sales": { "plan_digest": "48688476f7799492cdaea94081751cbc697d740f65d03d7b16d5b08ab3149470", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 21613, "n_filas_leidas": 21613, "n_test": 4323, "n_predictores": 21 }, "house_16H": { "plan_digest": "35ed9717d3061f760636d578379a2722aeccd220fea45f216839f8913b56edb1", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 22784, "n_filas_leidas": 22784, "n_test": 4557, "n_predictores": 16 }, "Amazon_employee_access": { "plan_digest": "a09ed2195c75e10687418e976301c2bf34472d37d9ee721766132a88ff5ea678", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 32769, "n_filas_leidas": 32769, "n_test": 6553, "n_predictores": 9 }, "KDDCup09_appetency": { "plan_digest": "57fdb98aad1d7a2e1dbc57b120d45971854b50086ed232db0d01598809dfa8c9", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 50000, "n_filas_leidas": 50000, "n_test": 10000, "n_predictores": 230 }, "okcupid-stem": { "plan_digest": "4c57aabc2174acedf40c324da4ecdda38516bf559792a05a0748e8ab3977a2a7", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 50789, "n_filas_leidas": 50789, "n_test": 10157, "n_predictores": 19 }, "diamonds": { "plan_digest": "62dc20a9095d37fdddcc867c59fc2b1472a9679a414cef11d655404147515b78", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 53940, "n_filas_leidas": 53940, "n_test": 10788, "n_predictores": 9 }, "connect-4": { "plan_digest": "ca82cc9250a77a600c0a6999e6361b9f86a88da4a3c9d5ec73b5c14be201fbba", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 67557, "n_filas_leidas": 67557, "n_test": 13512, "n_predictores": 42 }, "kick": { "plan_digest": "e07336e0e1c8c47657c603470339e671d04f9a92a76227b7468b9af258e35df6", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 72983, "n_filas_leidas": 72983, "n_test": 14596, "n_predictores": 32 }, "APSFailure": { "plan_digest": "78607228fe74d3f53d5562389384874383c5382ff764a594fdbce7354b15c8c4", "estratificado_por_el_objetivo": true, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 76000, "n_filas_leidas": 76000, "n_test": 15200, "n_predictores": 170 }, "Allstate_Claims_Severity": { "plan_digest": "303bb345c00a4be24c4edfb0157bdeb4cee120908d231ac6a56759e0e1d965cb", "estratificado_por_el_objetivo": false, "folds_pedidos": 5, "repeticiones_pedidas": 1, "folds_obtenidos": 5, "n_pliegues_pedidos": 5, "n_pliegues_obtenidos": 5, "limites": [], "n_filas_con_objetivo": 188318, "n_filas_leidas": 188318, "n_test": 37664, "n_predictores": 130 } }, "criterio_de_la_estratificacion": "El protocolo registra «5 folds estratificados x 3 repeticiones». La estratificacion reparte CLASES; `proponer_particion` la aplica cuando se le pasa `objetivo`, y con un objetivo CONTINUO cada valor distinto se vuelve un estrato de una sola fila. Medido el 2026-09-14 sobre los datasets reales del protocolo: en `Moneyball` (374 valores distintos) los pliegues bajan de 5 a 2 y el pliegue sale con entrena=407 frente a valida=593 (se entrena con menos de lo que se valida, y siempre los mismos porque los valores unicos caen todos en el mismo cubo); en `house_prices_nominal` (663 valores distintos) el test sale al 13,84 % en vez del 20 % pedido, porque round(1*0,2)=0 deja fuera de test toda fila cuyo valor de objetivo sea unico — el test deja de ser una muestra y pasa a ser «las filas cuyo precio se repite». Por eso aqui se estratifica solo en las dos tareas de clasificacion. NO se sustituye por una estratificacion en cuantiles: eso seria un diseno de particion distinto del registrado, y un diseno nuevo se registra, no se improvisa al medir.", "medidas_siempre_que_no_se_pueden_dar": { "tiempo_de_prediccion": "el harness llama a `motor.predict` dentro de `_muestra_de_test` sin cronometrarlo, y `Recursos` (matrixai-engines/particiones.py) solo tiene `wall_seconds` y `cpu_seconds` del AJUSTE. Darlo exige tocar `matrixai-engines`, que no es territorio de este fichero.", "rss_pico_mb": "el hijo de `ejecutar_intento_aislado` no devuelve su `ru_maxrss`: el padre recibe el `Intento` por la cola y nada mas. Se puede medir (`resource.getrusage(RUSAGE_CHILDREN)`) pero atribuir a UN intento el pico acumulado de todos los hijos seria un numero falso, y uno falso es peor que uno ausente." }, "donde_vive_cada_medida_siempre": { "tiempo_de_ajuste": { "estado": "guardada", "donde": "resultados[].tiempo_de_ajuste" }, "tiempo_de_prediccion": { "estado": "imposible", "motivo": "el harness llama a `motor.predict` dentro de `_muestra_de_test` sin cronometrarlo, y `Recursos` (matrixai-engines/particiones.py) solo tiene `wall_seconds` y `cpu_seconds` del AJUSTE. Darlo exige tocar `matrixai-engines`, que no es territorio de este fichero." }, "cpu_segundos": { "estado": "guardada", "donde": "resultados[].cpu_segundos" }, "rss_pico_mb": { "estado": "imposible", "motivo": "el hijo de `ejecutar_intento_aislado` no devuelve su `ru_maxrss`: el padre recibe el `Intento` por la cola y nada mas. Se puede medir (`resource.getrusage(RUSAGE_CHILDREN)`) pero atribuir a UN intento el pico acumulado de todos los hijos seria un numero falso, y uno falso es peor que uno ausente." }, "tasa_de_fallo": { "estado": "derivada", "donde": "aqui mismo, en `valor_por_motor`", "como": "intentos cuyo `estado` no esta en ESTADOS_QUE_CUENTAN_COMO_MEDIDA, sobre el total del motor", "valor_por_motor": { "baseline": { "intentos": 497, "fallidos": 0, "tasa": 0.0 }, "catboost": { "intentos": 497, "fallidos": 0, "tasa": 0.0 }, "lightgbm": { "intentos": 497, "fallidos": 0, "tasa": 0.0 }, "matrixai.dense.torch_cpu": { "intentos": 497, "fallidos": 10, "tasa": 0.02012072434607646 }, "sklearn.hgb": { "intentos": 497, "fallidos": 0, "tasa": 0.0 }, "sklearn.lineal": { "intentos": 497, "fallidos": 0, "tasa": 0.0 }, "xgboost": { "intentos": 497, "fallidos": 0, "tasa": 0.0 } } }, "dispersion_entre_semillas": { "estado": "derivada", "donde": "alcance_y_veredicto.por_motor..dispersion.sd_entre_semillas_mediana / sd_entre_semillas_maxima, y por dataset en .por_dataset..sd_entre_semillas" } }, "total_wall_s": 20939.7, "n_intentos": 3479, "por_que_los_procesos_no_son_los_registrados": { "registrados": 2, "usados": 1, "coincide": false, "conservadora_para_el_veredicto": true, "por_que": "MENOS procesos que los registrados: menos competencia por la maquina, o sea topes de reloj de pared mas faciles de cumplir, no mas dificiles. La desviacion no puede inflar el recuento de datasets cumplidos.", "donde_se_decide": "`PROCESOS_A_LA_VEZ` en `benchmarks/fase0/pasada_exploratoria_101_c3.py`. El protocolo registra el reparto; esta constante dice lo que la maquina de casa aguanta." }, "por_que_n_intentos_no_es_el_del_plan": { "plan": 3500, "medidos": 3479, "hueco": 21, "explicado_por_particiones_recortadas": 21, "resto_sin_explicar": 0, "cuadra": true, "por_dataset": [ { "dataset": "yeast", "pliegues_pedidos": 15, "pliegues_obtenidos": 12, "intentos_que_explica": 21, "limites": [ { "clave": "pliegues_reducidos_por_eventos", "campo": "objetivo", "medida": { "pliegues_pedidos": 5, "pliegues_usados": 4, "eventos_clase_minoritaria": 4 } } ] } ], "como_se_lee": "`plan.n_intentos` es lo que se pedia ANTES de particionar; `n_intentos` es lo que se pudo medir. La diferencia se explica dataset a dataset con los pliegues que su clase minoritaria no dio. Si `cuadra` es false, queda un hueco que ninguna particion justifica y eso SI es un problema." }, "n_reusados": 3369, "lectura_de_los_datos": { "23381": { "nombre": "dresses-sales", "objetivo": "Class", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "1063": { "nombre": "kc2", "objetivo": "problems", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "40994": { "nombre": "climate-model-simulation-crashes", "objetivo": "outcome", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "1515": { "nombre": "micro-mass", "objetivo": "Class", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "11": { "nombre": "balance-scale", "objetivo": "class", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": 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[], "n_del_protocolo": 7, "n_que_corrieron": 7 }, "datasets": { "criterio_del_subconjunto": "LOS 40 datasets del protocolo registrado, sin recorte: las tres tareas (20 binarias, 10 multiclase, 10 de regresion), los tres cubos de tamano (15 pequeno, 15 mediano, 10 grande) y LOS 8 SELLADOS, que hasta esta pasada no se habian tocado nunca a proposito. La lista NO esta escrita en este fichero: se deriva de `protocolo_exploratorio.json` y se contrasta contra el, de modo que un dataset que faltara se veria en `alcance.datasets.que_faltan` en vez de desaparecer sin ruido.", "del_protocolo": [ "kr-vs-kp", "letter", "balance-scale", "mfeat-factors", "optdigits", "pendigits", "diabetes", "sick", "splice", "yeast", "pol", "wine_quality", "house_16H", "pc4", "pc3", "jm1", "kc2", "pc1", "KDDCup09_appetency", "banknote-authentication", "ozone-level-8hr", "micro-mass", "Amazon_employee_access", "PhishingWebsites", "dresses-sales", "connect-4", "Satellite", "Internet-Advertisements", "wilt", "climate-model-simulation-crashes", "Moneyball", "APSFailure", "kick", "SAT11-HAND-runtime-regression", "diamonds", "house_prices_nominal", "Allstate_Claims_Severity", "us_crime", "house_sales", "okcupid-stem" ], "declarados_por_la_pasada": [ "dresses-sales", "kc2", "climate-model-simulation-crashes", "micro-mass", "balance-scale", "diabetes", "pc1", "Moneyball", "banknote-authentication", "pc4", "house_prices_nominal", "yeast", "pc3", "us_crime", "mfeat-factors", "ozone-level-8hr", "splice", "kr-vs-kp", "Internet-Advertisements", "sick", "SAT11-HAND-runtime-regression", "wilt", "Satellite", "optdigits", "wine_quality", "jm1", "pendigits", "PhishingWebsites", "pol", "letter", "house_sales", "house_16H", "Amazon_employee_access", "KDDCup09_appetency", "okcupid-stem", "diamonds", "connect-4", "kick", "APSFailure", "Allstate_Claims_Severity" ], "observados_en_los_resultados": [ "APSFailure", "Allstate_Claims_Severity", "Amazon_employee_access", "Internet-Advertisements", "KDDCup09_appetency", "Moneyball", "PhishingWebsites", "SAT11-HAND-runtime-regression", "Satellite", "balance-scale", "banknote-authentication", "climate-model-simulation-crashes", "connect-4", "diabetes", "diamonds", "dresses-sales", "house_16H", "house_prices_nominal", "house_sales", "jm1", "kc2", "kick", "kr-vs-kp", "letter", "mfeat-factors", "micro-mass", "okcupid-stem", "optdigits", "ozone-level-8hr", "pc1", "pc3", "pc4", "pendigits", "pol", "sick", "splice", "us_crime", "wilt", "wine_quality", "yeast" ], "que_faltan": [], "n_del_protocolo": 40, "n_que_corrieron": 40 }, "configuraciones": { "criterio_de_las_configuraciones": "UNA configuracion por motor (la `-default`) de las DOS que el protocolo registra por motor (13 en total contando el dummy, que registra una). El protocolo declara CUANTAS son (`Motor.configuraciones`) y su modelo de coste solo cuadra con 13, pero NO declara CUALES: el anexo C 2.3 las nombra —«defaults» y «busqueda bajo presupuesto»— y su propia medida anti-favoritismo numero 1 exige que los ESPACIOS DE BUSQUEDA esten registrados con sha256 ANTES de la primera pasada. No lo estan en `protocolo_exploratorio.json` ni en ningun otro sitio del arbol, y ninguno de los siete adaptadores implementa busqueda interna: son de hiperparametros FIJOS. Inventar aqui los espacios seria elegirlos DESPUES de ver los numeros, que es exactamente lo que el pre-registro existe para impedir. Se declara, no se esconde: de esta pasada no se sigue nada sobre «el rival en su mejor version».", "del_protocolo": { "dummy": 1, "sklearn.logreg": 2, "sklearn.hgb": 2, "lightgbm": 2, "xgboost": 2, "catboost": 2, "matrixai.dense.torch_cpu": 2 }, "n_del_protocolo": 13, "declaradas_por_la_pasada": { "baseline": [ "default" ], "sklearn.lineal": [ "default" ], "lightgbm": [ "default" ], "matrixai.dense.torch_cpu": [ "default" ], "sklearn.hgb": [ "default" ], "xgboost": [ "default" ], "catboost": [ "default" ] }, "n_que_corrieron": 7, "que_faltan_por_motor": { "dummy": 0, "sklearn.logreg": 1, "lightgbm": 1, "matrixai.dense.torch_cpu": 1, "sklearn.hgb": 1, "xgboost": 1, "catboost": 1 }, "observadas_en_los_resultados": { "baseline": [ "default" ], "catboost": [ "default" ], "lightgbm": [ "default" ], "matrixai.dense.torch_cpu": [ "default" ], "sklearn.hgb": [ "default" ], "sklearn.lineal": [ "default" ], "xgboost": [ "default" ] }, "observable_en_los_registros": true, "por_que_no_es_observable": "" }, "sin_medir": { "tareas": [], "cubos_de_tamano": [] } }, "metrica_de_cierre_por_dataset": { "dresses-sales": "auroc", "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "diabetes": "auroc", "pc1": "auroc", "Moneyball": "r2", "banknote-authentication": "auroc", "pc4": "auroc", "house_prices_nominal": "r2", "yeast": "accuracy", "pc3": "auroc", "us_crime": "r2", "mfeat-factors": "accuracy", "ozone-level-8hr": "auroc", "splice": "accuracy", "kr-vs-kp": "auroc", "Internet-Advertisements": "auroc", "sick": "auroc", "SAT11-HAND-runtime-regression": "r2", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "kick": "auroc", "APSFailure": "auroc", "Allstate_Claims_Severity": "r2" }, "metrica_de_cierre_por_tarea_registrada": { "binary_classification": "AUROC", "multiclass_classification": "accuracy_o_f1_macro", "regression": "R2" }, "de_la_metrica_registrada_al_metric_id": { "AUROC": "auroc", "accuracy_o_f1_macro": "accuracy", "R2": "r2" }, "estabilidad_del_ganador": { "metrica": "por tarea, ver `metrica_por_dataset`", "metrica_por_dataset": { "APSFailure": "auroc", "Allstate_Claims_Severity": "r2", "Amazon_employee_access": "auroc", "Internet-Advertisements": "auroc", "KDDCup09_appetency": "auroc", "Moneyball": "r2", "PhishingWebsites": "auroc", "SAT11-HAND-runtime-regression": "r2", "Satellite": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "dresses-sales": "auroc", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "auroc", "kick": "auroc", "kr-vs-kp": "auroc", "letter": "accuracy", "mfeat-factors": "accuracy", "micro-mass": "accuracy", "okcupid-stem": "accuracy", "optdigits": "accuracy", "ozone-level-8hr": "auroc", "pc1": "auroc", "pc3": "auroc", "pc4": "auroc", "pendigits": "accuracy", "pol": "r2", "sick": "auroc", "splice": "accuracy", "us_crime": "r2", "wilt": "auroc", "wine_quality": "r2", "yeast": "accuracy" }, "emparejado_por": "repeticion y pliegue", "n_datasets": 40, "datasets_en_los_que_la_ordenacion_se_discute": [ "Moneyball", "PhishingWebsites", "banknote-authentication", "diabetes", "house_prices_nominal", "kc2", "kick", "letter", "mfeat-factors", "micro-mass", "okcupid-stem", "ozone-level-8hr", "pc3", "sick", "wilt", "wine_quality" ], "datasets_en_los_que_el_mejor_por_media_PIERDE_la_mayoria_de_pliegues": [ "PhishingWebsites", "banknote-authentication", "kc2", "kick", "letter", "mfeat-factors", "okcupid-stem", "pc3", "sick", "wilt", "wine_quality" ], "detalle": [ { "dataset": "APSFailure", "mejor_por_media": "catboost", "segundo": "xgboost", "ventaja_en_puntos": 0.019703365311407595, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 4, "sd_de_la_diferencia": 0.09160212565671359, "ganador_por_semilla": { "0": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "Allstate_Claims_Severity", "mejor_por_media": "xgboost", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.570148270124804, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 5, "sd_de_la_diferencia": 0.2978085710396747, "ganador_por_semilla": { "0": "xgboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "Amazon_employee_access", "mejor_por_media": "lightgbm", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.31159266068532254, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 4, "sd_de_la_diferencia": 0.4906563076239879, "ganador_por_semilla": { "0": "lightgbm" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "Internet-Advertisements", "mejor_por_media": "sklearn.lineal", "segundo": "catboost", "ventaja_en_puntos": 0.7710838729571345, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 15, "sd_de_la_diferencia": 0.4453405619467233, "ganador_por_semilla": { "0": "sklearn.lineal", "1": "sklearn.lineal", "2": "sklearn.lineal" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "KDDCup09_appetency", "mejor_por_media": "catboost", "segundo": "sklearn.hgb", "ventaja_en_puntos": 4.271453215551424, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 5, "sd_de_la_diferencia": 0.9171269985033699, "ganador_por_semilla": { "0": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "Moneyball", "mejor_por_media": "sklearn.hgb", "segundo": "catboost", "ventaja_en_puntos": 0.03183270272505423, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 8, "sd_de_la_diferencia": 0.3391064575306641, "ganador_por_semilla": { "0": "sklearn.hgb", "1": "sklearn.hgb", "2": "catboost" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "PhishingWebsites", "mejor_por_media": "lightgbm", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.0029841343523684394, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 7, "sd_de_la_diferencia": 0.023286130255802926, "ganador_por_semilla": { "0": "lightgbm", "1": "lightgbm", "2": "sklearn.hgb" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "SAT11-HAND-runtime-regression", "mejor_por_media": "xgboost", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.550813683193363, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 11, "sd_de_la_diferencia": 0.6875901405162002, "ganador_por_semilla": { "0": "xgboost", "1": "xgboost", "2": "xgboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "Satellite", "mejor_por_media": "matrixai.dense.torch_cpu", "segundo": "sklearn.lineal", "ventaja_en_puntos": 1.6238805970149262, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 15, "sd_de_la_diferencia": 0.7911888464162882, "ganador_por_semilla": { "0": "matrixai.dense.torch_cpu", "1": "matrixai.dense.torch_cpu", "2": "matrixai.dense.torch_cpu" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "balance-scale", "mejor_por_media": "matrixai.dense.torch_cpu", "segundo": "catboost", "ventaja_en_puntos": 4.550264550264549, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 15, "sd_de_la_diferencia": 2.0448016851961053, "ganador_por_semilla": { "0": "matrixai.dense.torch_cpu", "1": "matrixai.dense.torch_cpu", "2": "matrixai.dense.torch_cpu" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "banknote-authentication", "mejor_por_media": "sklearn.hgb", "segundo": "lightgbm", "ventaja_en_puntos": 0.0032355478860934817, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 5, "sd_de_la_diferencia": 0.01149373345344512, "ganador_por_semilla": { "0": "sklearn.hgb", "1": "lightgbm", "2": "matrixai.dense.torch_cpu" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "climate-model-simulation-crashes", "mejor_por_media": "catboost", "segundo": "lightgbm", "ventaja_en_puntos": 1.0624766180321599, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 10, "sd_de_la_diferencia": 1.386867729088633, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "connect-4", "mejor_por_media": "sklearn.hgb", "segundo": "lightgbm", "ventaja_en_puntos": 1.3647128478389448, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 5, "sd_de_la_diferencia": 0.2413283093308599, "ganador_por_semilla": { "0": "sklearn.hgb" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "diabetes", "mejor_por_media": "catboost", "segundo": "matrixai.dense.torch_cpu", "ventaja_en_puntos": 1.0234567901234448, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 13, "sd_de_la_diferencia": 1.7889511456663, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "matrixai.dense.torch_cpu" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "diamonds", "mejor_por_media": "xgboost", "segundo": "lightgbm", "ventaja_en_puntos": 0.04372417689785246, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 4, "sd_de_la_diferencia": 0.03377842044814264, "ganador_por_semilla": { "0": "xgboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "dresses-sales", "mejor_por_media": "lightgbm", "segundo": "sklearn.lineal", "ventaja_en_puntos": 2.554734537493153, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 12, "sd_de_la_diferencia": 2.7497196759093265, "ganador_por_semilla": { "0": "lightgbm", "1": "lightgbm", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "house_16H", "mejor_por_media": "lightgbm", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.07165217152657055, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 3, "sd_de_la_diferencia": 0.47528226463321166, "ganador_por_semilla": { "0": "lightgbm" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "house_prices_nominal", "mejor_por_media": "catboost", "segundo": "xgboost", "ventaja_en_puntos": 0.26973003626983294, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 9, "sd_de_la_diferencia": 0.7390470495146261, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "xgboost" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "house_sales", "mejor_por_media": "sklearn.hgb", "segundo": "lightgbm", "ventaja_en_puntos": 0.16867634043601365, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 3, "sd_de_la_diferencia": 0.5021822248561736, "ganador_por_semilla": { "0": "sklearn.hgb" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "jm1", "mejor_por_media": "sklearn.hgb", "segundo": "lightgbm", "ventaja_en_puntos": 0.16944956957888335, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 8, "sd_de_la_diferencia": 0.6511204761308748, "ganador_por_semilla": { "0": "sklearn.hgb", "1": "sklearn.hgb", "2": "sklearn.hgb" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "kc2", "mejor_por_media": "matrixai.dense.torch_cpu", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.21419009370816644, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 7, "sd_de_la_diferencia": 2.9369347663854692, "ganador_por_semilla": { "0": "matrixai.dense.torch_cpu", "1": "sklearn.hgb", "2": "matrixai.dense.torch_cpu" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "kick", "mejor_por_media": "sklearn.hgb", "segundo": "catboost", "ventaja_en_puntos": 0.021597807796625013, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 2, "sd_de_la_diferencia": 0.2723189312200156, "ganador_por_semilla": { "0": "sklearn.hgb" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": true }, { "dataset": "kr-vs-kp", "mejor_por_media": "lightgbm", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.006413402702787341, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 12, "sd_de_la_diferencia": 0.012366182213613975, "ganador_por_semilla": { "0": "lightgbm", "1": "lightgbm", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "letter", "mejor_por_media": "sklearn.hgb", "segundo": "lightgbm", "ventaja_en_puntos": 0.04497751124437066, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 7, "sd_de_la_diferencia": 0.22512540669156758, "ganador_por_semilla": { "0": "sklearn.hgb", "1": "sklearn.hgb", "2": "sklearn.hgb" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": true }, { "dataset": "mfeat-factors", "mejor_por_media": "sklearn.lineal", "segundo": "lightgbm", "ventaja_en_puntos": 0.13333333333332975, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 7, "sd_de_la_diferencia": 0.646878953864153, "ganador_por_semilla": { "0": "sklearn.lineal", "1": "sklearn.lineal", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "micro-mass", "mejor_por_media": "sklearn.hgb", "segundo": "lightgbm", "ventaja_en_puntos": 0.11799410029498247, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 8, "sd_de_la_diferencia": 2.735126223994091, "ganador_por_semilla": { "0": "lightgbm", "1": "sklearn.hgb", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "okcupid-stem", "mejor_por_media": "catboost", "segundo": "lightgbm", "ventaja_en_puntos": 0.021659938958340508, "pliegues_emparejados": 5, "pliegues_que_le_gana_al_segundo": 1, "sd_de_la_diferencia": 0.16699538850775944, "ganador_por_semilla": { "0": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": true }, { "dataset": "optdigits", "mejor_por_media": "sklearn.hgb", "segundo": "lightgbm", "ventaja_en_puntos": 0.10083036773429477, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 9, "sd_de_la_diferencia": 0.2201630969710369, "ganador_por_semilla": { "0": "sklearn.hgb", "1": "sklearn.hgb", "2": "sklearn.hgb" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "ozone-level-8hr", "mejor_por_media": "xgboost", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.1907894736842053, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 10, "sd_de_la_diferencia": 0.925522852475879, "ganador_por_semilla": { "0": "lightgbm", "1": "xgboost", "2": "xgboost" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "pc1", "mejor_por_media": "catboost", "segundo": "lightgbm", "ventaja_en_puntos": 2.925566343042074, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 13, "sd_de_la_diferencia": 2.5092849079635884, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "pc3", "mejor_por_media": "catboost", "segundo": "xgboost", "ventaja_en_puntos": 0.3087930011862361, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 7, "sd_de_la_diferencia": 1.1449301106008534, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "xgboost" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "pc4", "mejor_por_media": "catboost", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.43764467592593004, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 11, "sd_de_la_diferencia": 0.6677324095485071, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "pendigits", "mejor_por_media": "lightgbm", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.13642564802183177, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 14, "sd_de_la_diferencia": 0.09873763498714033, "ganador_por_semilla": { "0": "lightgbm", "1": "lightgbm", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "pol", "mejor_por_media": "matrixai.dense.torch_cpu", "segundo": "lightgbm", "ventaja_en_puntos": 1.348898063789683, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 15, "sd_de_la_diferencia": 0.1034409964603852, "ganador_por_semilla": { "0": "matrixai.dense.torch_cpu", "1": "matrixai.dense.torch_cpu", "2": "matrixai.dense.torch_cpu" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "sick", "mejor_por_media": "catboost", "segundo": "lightgbm", "ventaja_en_puntos": 0.014124293785311437, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 6, "sd_de_la_diferencia": 0.5658266961267945, "ganador_por_semilla": { "0": "lightgbm", "1": "catboost", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "splice", "mejor_por_media": "xgboost", "segundo": "lightgbm", "ventaja_en_puntos": 0.146290491118084, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 11, "sd_de_la_diferencia": 0.28698288916430065, "ganador_por_semilla": { "0": "xgboost", "1": "xgboost", "2": "xgboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "us_crime", "mejor_por_media": "catboost", "segundo": "xgboost", "ventaja_en_puntos": 0.6537213997567681, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 11, "sd_de_la_diferencia": 1.5255854783614062, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "wilt", "mejor_por_media": "catboost", "segundo": "xgboost", "ventaja_en_puntos": 0.006718172657027566, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 6, "sd_de_la_diferencia": 0.09319329762647356, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "xgboost" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "wine_quality", "mejor_por_media": "xgboost", "segundo": "lightgbm", "ventaja_en_puntos": 0.06532817634633536, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 6, "sd_de_la_diferencia": 0.6489554729125773, "ganador_por_semilla": { "0": "lightgbm", "1": "xgboost", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "dataset": "yeast", "mejor_por_media": "catboost", "segundo": "matrixai.dense.torch_cpu", "ventaja_en_puntos": 2.2651006711409405, "pliegues_emparejados": 12, "pliegues_que_le_gana_al_segundo": 12, "sd_de_la_diferencia": 1.2402083005096731, "ganador_por_semilla": { "0": "catboost", "1": "catboost", "2": "catboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false } ], "como_hay_que_leer_este_numero": "La regla decide con la MEDIA, y eso no cambia. En 16 de 40 datasets la ordenacion primero/segundo se discute al mirar los pliegues emparejados o al separar las semillas; en 11 el mejor por media PIERDE la mayoria de los pliegues emparejados contra el segundo. Ahi «gano» significa «gano el promedio», no «gano»." }, "por_motor": { "sklearn.lineal": { "motor": "sklearn.lineal", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 1, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 14, "fraccion": 0.35, "cumple_la_regla": false, "aciertos_por_ser_el_mejor": 2, "datasets_que_puede_perder_sin_incumplir": null, "datasets_que_le_faltan_para_cumplir": 18, "definicion_de_mejor": "el motor con mejor media de los ajustes en ESE dataset, excluido el baseline dummy; un fallo (timeout/crash) cuenta como dataset perdido para ese motor", "detalle": [ { "dataset": "APSFailure", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.7446992538449946, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.3993531292827743, "alto": 2.1608064565250507, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Allstate_Claims_Severity", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 5.887249805631011, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.439609158439913, "alto": 6.2948781840803525, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Amazon_employee_access", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 27.79745344550686, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 26.885393936441268, "alto": 28.848293934988238, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Internet-Advertisements", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.0, "segundo": "catboost", "ventaja_sobre_el_segundo_en_puntos": 0.7710838729571345, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "KDDCup09_appetency", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 22.110997096634698, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 21.583077658729888, "alto": 22.545142153935554, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Moneyball", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 6.046358330151369, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.920675067188332, "alto": 6.162950680659, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "PhishingWebsites", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 1.4481257978414708, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.4321359494244499, "alto": 1.462777344894091, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "SAT11-HAND-runtime-regression", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 25.1697021070996, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 24.80260956653654, "alto": 25.517616917876317, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Satellite", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.6238805970149262, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.2311553344389161, "alto": 1.9812493090105032, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "balance-scale", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 8.148148148148149, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 7.3015873015873005, "alto": 8.94179894179894, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "banknote-authentication", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0298389416163225, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.02516537244751464, "alto": 0.03380248777682233, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "climate-model-simulation-crashes", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.122334455667784, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.5910961466517068, "alto": 1.6986531986531987, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "connect-4", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 14.391651865008859, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 14.251036116044991, "alto": 14.551509769094135, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "diabetes", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.5259259259259195, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.0345679012345672, "alto": 2.045771604938269, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "diamonds", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 6.340453257982248, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 6.323353370221669, "alto": 6.353374679187331, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "dresses-sales", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 2.554734537493153, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.2177066228790387, "alto": 3.8625821018062405, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "house_16H", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 39.79271527196037, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 39.50202103875155, "alto": 39.95300720060429, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_prices_nominal", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 3.041255609549265, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.551452108786825, "alto": 3.491148825481223, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_sales", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 18.907452807702583, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 18.308954934783703, "alto": 19.381090536279988, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "jm1", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 18.876959800309134, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 18.45806313654621, "alto": 19.270749242051235, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kc2", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 5.393000573723472, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.483744501816791, "alto": 7.91747944157583, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kick", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 14.421423813729739, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 14.288489387254092, "alto": 14.569292658412177, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kr-vs-kp", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.627335492948522, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.6093288832171734, "alto": 0.6431743071234599, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "letter", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 19.178743961352662, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 19.070423121772446, "alto": 19.285357321339326, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "mfeat-factors", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.13333333333332975, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "micro-mass", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 5.014749262536866, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.775811209439526, "alto": 6.430678466076695, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "okcupid-stem", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.888352860096476, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.744609628827407, "alto": 2.0715762528305657, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "optdigits", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 1.9691577698695228, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.7793594306049825, "alto": 2.147093712930011, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "ozone-level-8hr", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 1.8728070175438538, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.462664473684209, "alto": 2.2864692982456125, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc1", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 20.994606256742177, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 19.367799352750808, "alto": 22.647357065803668, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc3", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 6.6892793594306, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.721761936536181, "alto": 7.619291221826806, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc4", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 6.931423611111109, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 6.212818287037036, "alto": 7.594907407407405, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pendigits", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 4.841594664241322, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 4.71721994846142, "alto": 4.9598302258602365, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "pol", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 53.88333334490337, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 53.851304475684046, "alto": 53.914734183728804, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "sick", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 3.550315237861301, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.2565299271268326, "alto": 3.868633832801113, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "splice", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 2.7272727272727337, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.4239811912225697, "alto": 3.0512016718913246, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "us_crime", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.8517377909415433, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.294961861686621, "alto": 2.413621949576157, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "wilt", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.3227802037845682, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.2981399059455834, "alto": 1.3514758985555944, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "wine_quality", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 12.690176423225308, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 12.312214577022273, "alto": 13.07434909535688, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "yeast", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 5.369127516778526, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 4.725950782997763, "alto": 5.985039149888141, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 12, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null } ], "metrica_por_dataset": { "dresses-sales": "auroc", "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "diabetes": "auroc", "pc1": "auroc", "Moneyball": "r2", "banknote-authentication": "auroc", "pc4": "auroc", "house_prices_nominal": "r2", "yeast": "accuracy", "pc3": "auroc", "us_crime": "r2", "mfeat-factors": "accuracy", "ozone-level-8hr": "auroc", "splice": "accuracy", "kr-vs-kp": "auroc", "Internet-Advertisements": "auroc", "sick": "auroc", "SAT11-HAND-runtime-regression": "r2", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "kick": "auroc", "APSFailure": "auroc", "Allstate_Claims_Severity": "r2" }, "datasets_sin_metrica_declarada": [], "datasets_exigidos_que_no_se_midieron": [], "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`.", "dispersion": { "motor": "sklearn.lineal", "metrica": "auroc", "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "n_datasets": 40, "sd_mediana": 0.42102477341282685, "sd_maxima": 4.193380128751064, "rango_mediano": 1.1638098322085817, "rango_maximo": 12.679288582903041, "dataset_mas_disperso": "kc2", "sd_entre_semillas_mediana": 0.09459528509639553, "sd_entre_semillas_maxima": 0.5314715224381511, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 14, "cuales_con_rango_mayor_que_el_liston": [ "Amazon_employee_access", "Satellite", "balance-scale", "climate-model-simulation-crashes", "dresses-sales", "house_prices_nominal", "kc2", "micro-mass", "ozone-level-8hr", "pc1", "pc3", "pc4", "splice", "yeast" ], "por_dataset": { "APSFailure": { "n_medidas": 5, "media": 97.7788213796254, "sd": 0.4670679547824876, "rango": 1.2020222323739915, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Allstate_Claims_Severity": { "n_medidas": 5, "media": 50.932684343240865, "sd": 0.28478220635900175, "rango": 0.7642930896776576, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Amazon_employee_access": { "n_medidas": 5, "media": 54.46833388462811, "sd": 1.1640888822711504, "rango": 2.547024589456335, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Internet-Advertisements": { "n_medidas": 15, "media": 98.76978620618769, "sd": 0.2626607942361016, "rango": 1.056120875732347, "n_semillas": 3, "sd_entre_semillas": 0.06682156052554598, "rango_entre_semillas": 0.12372802960220497 }, "KDDCup09_appetency": { "n_medidas": 5, "media": 59.61346804582238, "sd": 0.4580498788744835, "rango": 1.1255974320431719, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Moneyball": { "n_medidas": 15, "media": 87.30966288652954, "sd": 0.1439489458056402, "rango": 0.5164385205947895, "n_semillas": 3, "sd_entre_semillas": 0.004712752794661715, "rango_entre_semillas": 0.009347491746538594 }, "PhishingWebsites": { "n_medidas": 15, "media": 97.9894450062722, "sd": 0.016971035925290447, "rango": 0.05984847228900492, "n_semillas": 3, "sd_entre_semillas": 0.003087762182199581, "rango_entre_semillas": 0.006100896898161068 }, "SAT11-HAND-runtime-regression": { "n_medidas": 15, "media": 50.02073591093826, "sd": 0.3070070634780617, "rango": 0.9576207977118472, "n_semillas": 3, "sd_entre_semillas": 0.01839752680259321, "rango_entre_semillas": 0.03480410060105754 }, "Satellite": { "n_medidas": 15, "media": 97.5566611387507, "sd": 0.7708068405671984, "rango": 2.593698175787736, "n_semillas": 3, "sd_entre_semillas": 0.30453276567081855, "rango_entre_semillas": 0.6049751243781003 }, "balance-scale": { "n_medidas": 15, "media": 86.45502645502646, "sd": 1.1797196078685834, "rango": 5.555555555555557, "n_semillas": 3, "sd_entre_semillas": 0.510246072010206, "rango_entre_semillas": 0.952380952380949 }, "banknote-authentication": { "n_medidas": 15, "media": 99.95434282427378, "sd": 0.0060691483376969745, "rango": 0.016177739430546012, "n_semillas": 3, "sd_entre_semillas": 0.0027142056497280634, "rango_entre_semillas": 0.005392579810191478 }, "climate-model-simulation-crashes": { "n_medidas": 15, "media": 94.87467265245043, "sd": 1.0644582384136696, "rango": 3.479236812570136, "n_semillas": 3, "sd_entre_semillas": 0.21569076401645854, "rango_entre_semillas": 0.4264870931537672 }, "connect-4": { "n_medidas": 5, "media": 66.17229129662522, "sd": 0.036407264287293135, "rango": 0.08140911782119531, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "diabetes": { "n_medidas": 15, "media": 78.40987654320989, "sd": 0.38399966795117024, "rango": 1.3703703703703667, "n_semillas": 3, "sd_entre_semillas": 0.0741974281546504, "rango_entre_semillas": 0.14074074074073906 }, "diamonds": { "n_medidas": 5, "media": 91.78996974901425, "sd": 0.00790945407485801, "rango": 0.021101208353385914, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "dresses-sales": { "n_medidas": 15, "media": 55.48987411056376, "sd": 3.2919601884967036, "rango": 11.247947454844002, "n_semillas": 3, "sd_entre_semillas": 0.48393658250134797, "rango_entre_semillas": 0.9359605911330107 }, "house_16H": { "n_medidas": 5, "media": 26.434958340063588, "sd": 0.0899381265931104, "rango": 0.22163616480136383, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "house_prices_nominal": { "n_medidas": 15, "media": 88.568931745705, "sd": 0.6621915992731255, "rango": 2.2713497779418503, "n_semillas": 3, "sd_entre_semillas": 0.005286352175009537, "rango_entre_semillas": 0.009692313876257685 }, "house_sales": { "n_medidas": 5, "media": 68.05915438462183, "sd": 0.04406897804226307, "rango": 0.10916812143057086, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "jm1": { "n_medidas": 15, "media": 55.558018926625515, "sd": 0.5035829747704138, "rango": 1.64471726391767, "n_semillas": 3, "sd_entre_semillas": 0.2966126255940093, "rango_entre_semillas": 0.5757524929796105 }, "kc2": { "n_medidas": 15, "media": 86.15414037100784, "sd": 4.193380128751064, "rango": 12.679288582903041, "n_semillas": 3, "sd_entre_semillas": 0.5314715224381511, "rango_entre_semillas": 1.0327022375215194 }, "kick": { "n_medidas": 5, "media": 62.18785745107395, "sd": 0.07588698192236477, "rango": 0.17688816529175, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "kr-vs-kp": { "n_medidas": 15, "media": 99.35440594221394, "sd": 0.036916027399664486, "rango": 0.13546677137527752, "n_semillas": 3, "sd_entre_semillas": 0.003339511356871321, "rango_entre_semillas": 0.006282516933339366 }, "letter": { "n_medidas": 15, "media": 76.08695652173914, "sd": 0.12600387373972238, "rango": 0.4747626186906473, "n_semillas": 3, "sd_entre_semillas": 0.01499250374812533, "rango_entre_semillas": 0.02998500749625066 }, "mfeat-factors": { "n_medidas": 15, "media": 97.85, "sd": 0.5328494829016647, "rango": 2.0, "n_semillas": 3, "sd_entre_semillas": 0.25, "rango_entre_semillas": 0.5 }, "micro-mass": { "n_medidas": 15, "media": 81.47492625368731, "sd": 2.30444795282048, "rango": 7.079646017699119, "n_semillas": 3, "sd_entre_semillas": 0.5109294417607321, "rango_entre_semillas": 0.8849557522123916 }, "okcupid-stem": { "n_medidas": 5, "media": 73.68120508024023, "sd": 0.23208910081514167, "rango": 0.5414984739588391, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "optdigits": { "n_medidas": 15, "media": 96.90391459074732, "sd": 0.3103876738254125, "rango": 0.9786476868327441, "n_semillas": 3, "sd_entre_semillas": 0.030819409387341023, "rango_entre_semillas": 0.05338078291813986 }, "ozone-level-8hr": { "n_medidas": 15, "media": 91.7921052631579, "sd": 0.583043651101551, "rango": 2.1118421052631504, "n_semillas": 3, "sd_entre_semillas": 0.10841498450060216, "rango_entre_semillas": 0.20921052631578618 }, "pc1": { "n_medidas": 15, "media": 60.073354908306364, "sd": 1.7445937291052314, "rango": 7.76699029126214, "n_semillas": 3, "sd_entre_semillas": 0.38451904515580465, "rango_entre_semillas": 0.7378640776699115 }, "pc3": { "n_medidas": 15, "media": 74.34979240806642, "sd": 2.329936403794497, "rango": 8.685498220640554, "n_semillas": 3, "sd_entre_semillas": 0.15061185366218832, "rango_entre_semillas": 0.2802491103202698 }, "pc4": { "n_medidas": 15, "media": 86.66160300925927, "sd": 1.174974448584348, "rango": 3.5807291666666714, "n_semillas": 3, "sd_entre_semillas": 0.17528937812182774, "rango_entre_semillas": 0.34939236111111427 }, "pendigits": { "n_medidas": 15, "media": 94.06700015158407, "sd": 0.15927149354632253, "rango": 0.5002273760800335, "n_semillas": 3, "sd_entre_semillas": 0.10619542148332209, "rango_entre_semillas": 0.20918599363346857 }, "pol": { "n_medidas": 15, "media": 45.69131054678779, "sd": 0.06976085988037534, "rango": 0.25303044784642736, "n_semillas": 3, "sd_entre_semillas": 0.011178788592708174, "rango_entre_semillas": 0.02208719759149602 }, "sick": { "n_medidas": 15, "media": 95.90415950216983, "sd": 0.6863493875502106, "rango": 1.9252026529108264, "n_semillas": 3, "sd_entre_semillas": 0.08299514870946897, "rango_entre_semillas": 0.15106853352983762 }, "splice": { "n_medidas": 15, "media": 92.97805642633229, "sd": 0.6495050544531408, "rango": 2.1943573667711576, "n_semillas": 3, "sd_entre_semillas": 0.1366426001109945, "rango_entre_semillas": 0.25078369905956777 }, "us_crime": { "n_medidas": 15, "media": 70.39130252197684, "sd": 0.5004736023933576, "rango": 1.7023901704793474, "n_semillas": 3, "sd_entre_semillas": 0.05007224277244177, "rango_entre_semillas": 0.09160368710890054 }, "wilt": { "n_medidas": 15, "media": 98.44978165938865, "sd": 0.047787462059149306, "rango": 0.19524689284513386, "n_semillas": 3, "sd_entre_semillas": 0.006937651848274613, "rango_entre_semillas": 0.012596573731940452 }, "wine_quality": { "n_medidas": 15, "media": 30.392614855703737, "sd": 0.14208357498277047, "rango": 0.45584756943485516, "n_semillas": 3, "sd_entre_semillas": 0.0030189873808727646, "rango_entre_semillas": 0.006036347654220009 }, "yeast": { "n_medidas": 12, "media": 55.06152125279642, "sd": 0.5249259362251769, "rango": 2.0134228187919447, "n_semillas": 3, "sd_entre_semillas": 0.2421771263379275, "rango_entre_semillas": 0.4194630872483174 } }, "metrica_por_dataset": { "APSFailure": "auroc", "Allstate_Claims_Severity": "r2", "Amazon_employee_access": "auroc", "Internet-Advertisements": "auroc", "KDDCup09_appetency": "auroc", "Moneyball": "r2", "PhishingWebsites": "auroc", "SAT11-HAND-runtime-regression": "r2", "Satellite": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "dresses-sales": "auroc", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "auroc", "kick": "auroc", "kr-vs-kp": "auroc", "letter": "accuracy", "mfeat-factors": "accuracy", "micro-mass": "accuracy", "okcupid-stem": "accuracy", "optdigits": "accuracy", "ozone-level-8hr": "auroc", "pc1": "auroc", "pc3": "auroc", "pc4": "auroc", "pendigits": "accuracy", "pol": "r2", "sick": "auroc", "splice": "accuracy", "us_crime": "r2", "wilt": "auroc", "wine_quality": "r2", "yeast": "accuracy" }, "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`. Y ojo al agregar: `rango_mediano`, `rango_maximo` y las desviaciones mezclan escalas de tareas distintas bajo una sola cifra.", "como_hay_que_leer_este_numero": "sklearn.lineal: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.421 puntos (maxima 4.193, en kc2); rango mediano 1.164 puntos y maximo 12.679. En 14 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.095, maxima 0.531 puntos." }, "como_hay_que_leer_este_numero": "Cada dataset se midio con la metrica de SU tarea (accuracy, auroc, r2), no con una sola para todos. Todos los datasets exigidos tienen medida. 14/40 sobre un ALCANCE RECORTADO: 7 de 7 motores del protocolo y 40 de 40 datasets. Configuraciones: 7 de 13 del protocolo (catboost: falta(n) 1; lightgbm: falta(n) 1; matrixai.dense.torch_cpu: falta(n) 1; sklearn.hgb: falta(n) 1; sklearn.logreg: falta(n) 1; xgboost: falta(n) 1); la regla mide «la mejor media de los AJUSTES» y aqui cada motor aporta 1 familia(s) de ajuste. NO corrieron: ninguno. Compitieron por el primer puesto 6 motores (el baseline no compite, la regla lo excluye). 2 de los 14 aciertos lo son por SER el mejor de esos, o sea distancia 0,0000: mirar `ventaja_sobre_el_segundo_en_puntos` antes de leerlos como dominio. NO cumple: le faltan 18 dataset(s) para llegar al liston. Sin medir: ninguna tarea; cubos todos cubiertos. sklearn.lineal: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.421 puntos (maxima 4.193, en kc2); rango mediano 1.164 puntos y maximo 12.679. En 14 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.095, maxima 0.531 puntos." }, "lightgbm": { "motor": "lightgbm", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 4, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 33, "fraccion": 0.825, "cumple_la_regla": true, "aciertos_por_ser_el_mejor": 6, "datasets_que_puede_perder_sin_incumplir": 1, "datasets_que_le_faltan_para_cumplir": null, "definicion_de_mejor": "el motor con mejor media de los ajustes en ESE dataset, excluido el baseline dummy; un fallo (timeout/crash) cuenta como dataset perdido para ese motor", "detalle": [ { "dataset": "APSFailure", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.257751789249272, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.00873701842545982, "alto": 0.6400140094411473, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Allstate_Claims_Severity", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.6014946206923222, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.2216549029855508, "alto": 0.939759499387003, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Amazon_employee_access", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.31159266068532254, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Internet-Advertisements", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 1.1420752389762479, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.0434814729160242, "alto": 1.247465695343816, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "KDDCup09_appetency", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 8.135216974505754, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 7.736719220095223, "alto": 8.525798539852062, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Moneyball", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.06116605229565808, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.020611216272211255, "alto": 0.14026408996403306, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "PhishingWebsites", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.0029841343523684394, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "SAT11-HAND-runtime-regression", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 1.7168831469072665, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.3936452493002065, "alto": 2.0153993573238718, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Satellite", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 2.079380873410719, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.357534549474848, "alto": 2.8888225538971812, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "balance-scale", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 7.354497354497358, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 6.243386243386241, "alto": 8.51984126984127, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "banknote-authentication", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0032355478860934817, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.0014380212827151222, "alto": 0.009347138337648294, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "climate-model-simulation-crashes", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.0624766180321599, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.35895997007108205, "alto": 1.6984661429105885, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "connect-4", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 1.3647128478389448, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.2063351095322683, "alto": 1.5837773830669, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "diabetes", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 3.5580246913580194, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.893672839506169, "alto": 4.29509259259259, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "diamonds", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.04372417689785246, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.01592850418974901, "alto": 0.072598872485381, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "dresses-sales", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.0, "segundo": "sklearn.lineal", "ventaja_sobre_el_segundo_en_puntos": 2.554734537493153, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "house_16H", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.07165217152657055, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_prices_nominal", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.4522428906593055, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.106779451469542, "alto": 1.811862562898308, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_sales", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.16867634043601365, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.195845834124444, "alto": 0.5383840512895333, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "jm1", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.16944956957888335, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.14218956564711083, "alto": 0.49074330831786717, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kc2", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 2.761522279594575, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.4416714476955426, "alto": 4.157678332377128, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kick", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.70679671395798, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.48724431565343895, "alto": 0.8838406818408862, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kr-vs-kp", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.006413402702787341, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "letter", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.04497751124437066, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.06671664167916257, "alto": 0.15996168582375522, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "mfeat-factors", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.13333333333332975, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.18333333333333313, "alto": 0.4337500000000014, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "micro-mass", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.11799410029498247, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -1.238938053097347, "alto": 1.533923303834807, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "okcupid-stem", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.021659938958340508, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.08860884119326728, "alto": 0.17721768238653457, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "optdigits", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.10083036773429477, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.011862396204035766, "alto": 0.2075919335705797, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "ozone-level-8hr", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.2662280701754338, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.2865241228070191, "alto": 0.8399232456140354, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc1", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.925566343042074, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.6954692556634292, "alto": 4.121305285868393, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc3", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.8166518386714117, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.14632451067615798, "alto": 1.442810275800713, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc4", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.5179398148148051, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.21336082175925813, "alto": 0.8203667534722214, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pendigits", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.13642564802183177, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "pol", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.348898063789683, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.2985675025975254, "alto": 1.3975581036423514, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "sick", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.014124293785311437, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.2471649062474441, "alto": 0.30242364693359397, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "splice", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.146290491118084, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.01018808777429251, "alto": 0.2716823406478556, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "us_crime", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.7348855817552402, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.0072433322187878246, "alto": 1.4395308414849255, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "wilt", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.17971111857574273, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.1232924644496693, "alto": 0.24592710782667018, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "wine_quality", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.06532817634633536, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.23117367273994036, "alto": 0.3899391872951685, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "yeast", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.4888143176733823, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.0339765100671146, "alto": 3.914988814317672, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 12, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null } ], "metrica_por_dataset": { "dresses-sales": "auroc", "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "diabetes": "auroc", "pc1": "auroc", "Moneyball": "r2", "banknote-authentication": "auroc", "pc4": "auroc", "house_prices_nominal": "r2", "yeast": "accuracy", "pc3": "auroc", "us_crime": "r2", "mfeat-factors": "accuracy", "ozone-level-8hr": "auroc", "splice": "accuracy", "kr-vs-kp": "auroc", "Internet-Advertisements": "auroc", "sick": "auroc", "SAT11-HAND-runtime-regression": "r2", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "kick": "auroc", "APSFailure": "auroc", "Allstate_Claims_Severity": "r2" }, "datasets_sin_metrica_declarada": [], "datasets_exigidos_que_no_se_midieron": [], "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`.", "dispersion": { "motor": "lightgbm", "metrica": "auroc", "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "n_datasets": 40, "sd_mediana": 0.5307608980638973, "sd_maxima": 3.3611094680904894, "rango_mediano": 1.4257983944928583, "rango_maximo": 9.935275080906152, "dataset_mas_disperso": "pc1", "sd_entre_semillas_mediana": 0.186339718841962, "sd_entre_semillas_maxima": 1.1261268070789852, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 18, "cuales_con_rango_mayor_que_el_liston": [ "SAT11-HAND-runtime-regression", "Satellite", "balance-scale", "climate-model-simulation-crashes", "diabetes", "dresses-sales", "house_prices_nominal", "jm1", "kc2", "micro-mass", "ozone-level-8hr", "pc1", "pc3", "pc4", "sick", "us_crime", "wine_quality", "yeast" ], "por_dataset": { "APSFailure": { "n_medidas": 5, "media": 99.26576884422111, "sd": 0.4034276531988066, "rango": 0.9575879396984988, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Allstate_Claims_Severity": { "n_medidas": 5, "media": 56.21843952817956, "sd": 0.1425795724005067, "rango": 0.39568568251855396, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Amazon_employee_access": { "n_medidas": 5, "media": 82.26578733013497, "sd": 0.6166931300168296, "rango": 1.6015967889857166, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Internet-Advertisements": { "n_medidas": 15, "media": 97.62771096721143, "sd": 0.27435617584712085, "rango": 1.0792476102374309, "n_semillas": 3, "sd_entre_semillas": 0.054635918886906276, "rango_entre_semillas": 0.10831020659881574 }, "KDDCup09_appetency": { "n_medidas": 5, "media": 73.58924816795133, "sd": 0.4866680889323272, "rango": 1.0427748759377664, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Moneyball": { "n_medidas": 15, "media": 93.29485516438523, "sd": 0.1936242538476148, "rango": 0.591754169852365, "n_semillas": 3, "sd_entre_semillas": 0.011160083976372834, "rango_entre_semillas": 0.022314639538407732 }, "PhishingWebsites": { "n_medidas": 15, "media": 99.43757080411368, "sd": 0.02708562466619609, "rango": 0.08811485601552249, "n_semillas": 3, "sd_entre_semillas": 0.0040507473294697375, "rango_entre_semillas": 0.008073741275538282 }, "SAT11-HAND-runtime-regression": { "n_medidas": 15, "media": 73.4735548711306, "sd": 0.7109356119624828, "rango": 2.6741951915674207, "n_semillas": 3, "sd_entre_semillas": 0.11185583946526116, "rango_entre_semillas": 0.21692866796416865 }, "Satellite": { "n_medidas": 15, "media": 97.10116086235489, "sd": 1.466243423753632, "rango": 5.731343283582092, "n_semillas": 3, "sd_entre_semillas": 0.3688869743422585, "rango_entre_semillas": 0.646102819237143 }, "balance-scale": { "n_medidas": 15, "media": 87.24867724867725, "sd": 1.7610840223045807, "rango": 5.555555555555557, "n_semillas": 3, "sd_entre_semillas": 1.1261268070789852, "rango_entre_semillas": 2.2222222222222285 }, "banknote-authentication": { "n_medidas": 15, "media": 99.98094621800402, "sd": 0.009531008430636671, "rango": 0.03774805867126929, "n_semillas": 3, "sd_entre_semillas": 0.004863297835231268, "rango_entre_semillas": 0.009706643658333292 }, "climate-model-simulation-crashes": { "n_medidas": 15, "media": 94.93453049008605, "sd": 1.3593469094255184, "rango": 4.48933782267116, "n_semillas": 3, "sd_entre_semillas": 0.39222872943003967, "rango_entre_semillas": 0.7631874298540993 }, "connect-4": { "n_medidas": 5, "media": 79.19923031379514, "sd": 0.07186810767502089, "rango": 0.18502072232091393, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "diabetes": { "n_medidas": 15, "media": 76.37777777777778, "sd": 2.0286117248207822, "rango": 6.962962962962962, "n_semillas": 3, "sd_entre_semillas": 0.2024879545605365, "rango_entre_semillas": 0.3962962962962848 }, "diamonds": { "n_medidas": 5, "media": 98.08669883009864, "sd": 0.039752199188966636, "rango": 0.10412026634458016, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "dresses-sales": { "n_medidas": 15, "media": 58.04460864805693, "sd": 2.451082047267917, "rango": 7.655993431855499, "n_semillas": 3, "sd_entre_semillas": 0.5546937618694415, "rango_entre_semillas": 1.0467980295566548 }, "house_16H": { "n_medidas": 5, "media": 66.22767361202395, "sd": 0.3729763128977216, "rango": 0.9032532239725697, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "house_prices_nominal": { "n_medidas": 15, "media": 90.15794446459496, "sd": 0.7825754393064895, "rango": 2.620765790796767, "n_semillas": 3, "sd_entre_semillas": 0.20033084573031745, "rango_entre_semillas": 0.4003396216933055 }, "house_sales": { "n_medidas": 5, "media": 86.7979308518884, "sd": 0.7581019181889381, "rango": 1.9573443316858032, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "jm1": { "n_medidas": 15, "media": 74.26552915735576, "sd": 0.5748537071954674, "rango": 2.3972778772745187, "n_semillas": 3, "sd_entre_semillas": 0.11321354360446888, "rango_entre_semillas": 0.21596805523240903 }, "kc2": { "n_medidas": 15, "media": 88.78561866513674, "sd": 2.6676481310872164, "rango": 9.294320137693632, "n_semillas": 3, "sd_entre_semillas": 0.8979703949453764, "rango_entre_semillas": 1.7900172117039403 }, "kick": { "n_medidas": 5, "media": 75.90248455084573, "sd": 0.22503330437243763, "rango": 0.5014993823384657, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "kr-vs-kp": { "n_medidas": 15, "media": 99.98174143516248, "sd": 0.024093293737732172, "rango": 0.08343967802100849, "n_semillas": 3, "sd_entre_semillas": 0.004528344986613311, "rango_entre_semillas": 0.008638460783345181 }, "letter": { "n_medidas": 15, "media": 95.22072297184741, "sd": 0.23315722331789104, "rango": 0.9995002498750694, "n_semillas": 3, "sd_entre_semillas": 0.08456465198398765, "rango_entre_semillas": 0.16491754122938573 }, "mfeat-factors": { "n_medidas": 15, "media": 97.71666666666667, "sd": 0.41042950789676536, "rango": 1.25, "n_semillas": 3, "sd_entre_semillas": 0.07637626158259299, "rango_entre_semillas": 0.14999999999999147 }, "micro-mass": { "n_medidas": 15, "media": 86.3716814159292, "sd": 2.23878064436699, "rango": 8.849557522123874, "n_semillas": 3, "sd_entre_semillas": 0.4682745683300151, "rango_entre_semillas": 0.8849557522123916 }, "okcupid-stem": { "n_medidas": 5, "media": 75.54789800137836, "sd": 0.15005799953366503, "rango": 0.38397164517083127, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "optdigits": { "n_medidas": 15, "media": 98.77224199288257, "sd": 0.28374267945858084, "rango": 1.156583629893234, "n_semillas": 3, "sd_entre_semillas": 0.0990705402638773, "rango_entre_semillas": 0.19572953736654597 }, "ozone-level-8hr": { "n_medidas": 15, "media": 93.39868421052633, "sd": 0.7573296373836678, "rango": 2.6118421052631646, "n_semillas": 3, "sd_entre_semillas": 0.282508916830322, "rango_entre_semillas": 0.5118421052631561 }, "pc1": { "n_medidas": 15, "media": 78.14239482200648, "sd": 3.3611094680904894, "rango": 9.935275080906152, "n_semillas": 3, "sd_entre_semillas": 0.23524636693056541, "rango_entre_semillas": 0.45954692556634313 }, "pc3": { "n_medidas": 15, "media": 80.22241992882563, "sd": 1.8196903528058295, "rango": 7.439946619217082, "n_semillas": 3, "sd_entre_semillas": 0.8434460197106127, "rango_entre_semillas": 1.658140569395016 }, "pc4": { "n_medidas": 15, "media": 93.07508680555556, "sd": 0.7523754252268501, "rango": 2.723524305555557, "n_semillas": 3, "sd_entre_semillas": 0.20137906690009252, "rango_entre_semillas": 0.39930555555555713 }, "pendigits": { "n_medidas": 15, "media": 98.90859481582538, "sd": 0.1630598375626727, "rango": 0.6366530241018751, "n_semillas": 3, "sd_entre_semillas": 0.07218966742331721, "rango_entre_semillas": 0.13642564802182733 }, "pol": { "n_medidas": 15, "media": 98.22574582790146, "sd": 0.08006921581806359, "rango": 0.35071812795293056, "n_semillas": 3, "sd_entre_semillas": 0.015947306017768777, "rango_entre_semillas": 0.03189381656164869 }, "sick": { "n_medidas": 15, "media": 99.4403504462458, "sd": 0.723393606484115, "rango": 2.8770572340948206, "n_semillas": 3, "sd_entre_semillas": 0.22140005757504472, "rango_entre_semillas": 0.40591992139523825 }, "splice": { "n_medidas": 15, "media": 95.55903866248694, "sd": 0.26272153913715607, "rango": 0.7836990595611297, "n_semillas": 3, "sd_entre_semillas": 0.06525598744408369, "rango_entre_semillas": 0.125391849529791 }, "us_crime": { "n_medidas": 15, "media": 71.50815473116315, "sd": 1.190199247816366, "rango": 3.823610699622094, "n_semillas": 3, "sd_entre_semillas": 0.17234859195360655, "rango_entre_semillas": 0.3420418011585298 }, "wilt": { "n_medidas": 15, "media": 99.59285074459747, "sd": 0.11498078546353263, "rango": 0.44088008061807216, "n_semillas": 3, "sd_entre_semillas": 0.027037297821703013, "rango_entre_semillas": 0.047447094390349775 }, "wine_quality": { "n_medidas": 15, "media": 43.01746310258271, "sd": 0.8523443106305014, "rango": 2.906379358398702, "n_semillas": 3, "sd_entre_semillas": 0.27415811751964025, "rango_entre_semillas": 0.5104999998286033 }, "yeast": { "n_medidas": 12, "media": 57.94183445190157, "sd": 2.114279572574332, "rango": 7.04697986577181, "n_semillas": 3, "sd_entre_semillas": 0.571045242076682, "rango_entre_semillas": 1.0906040268456394 } }, "metrica_por_dataset": { "APSFailure": "auroc", "Allstate_Claims_Severity": "r2", "Amazon_employee_access": "auroc", "Internet-Advertisements": "auroc", "KDDCup09_appetency": "auroc", "Moneyball": "r2", "PhishingWebsites": "auroc", "SAT11-HAND-runtime-regression": "r2", "Satellite": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "dresses-sales": "auroc", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "auroc", "kick": "auroc", "kr-vs-kp": "auroc", "letter": "accuracy", "mfeat-factors": "accuracy", "micro-mass": "accuracy", "okcupid-stem": "accuracy", "optdigits": "accuracy", "ozone-level-8hr": "auroc", "pc1": "auroc", "pc3": "auroc", "pc4": "auroc", "pendigits": "accuracy", "pol": "r2", "sick": "auroc", "splice": "accuracy", "us_crime": "r2", "wilt": "auroc", "wine_quality": "r2", "yeast": "accuracy" }, "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`. Y ojo al agregar: `rango_mediano`, `rango_maximo` y las desviaciones mezclan escalas de tareas distintas bajo una sola cifra.", "como_hay_que_leer_este_numero": "lightgbm: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.531 puntos (maxima 3.361, en pc1); rango mediano 1.426 puntos y maximo 9.935. En 18 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.186, maxima 1.126 puntos." }, "como_hay_que_leer_este_numero": "Cada dataset se midio con la metrica de SU tarea (accuracy, auroc, r2), no con una sola para todos. Todos los datasets exigidos tienen medida. 33/40 sobre un ALCANCE RECORTADO: 7 de 7 motores del protocolo y 40 de 40 datasets. Configuraciones: 7 de 13 del protocolo (catboost: falta(n) 1; lightgbm: falta(n) 1; matrixai.dense.torch_cpu: falta(n) 1; sklearn.hgb: falta(n) 1; sklearn.logreg: falta(n) 1; xgboost: falta(n) 1); la regla mide «la mejor media de los AJUSTES» y aqui cada motor aporta 1 familia(s) de ajuste. NO corrieron: ninguno. Compitieron por el primer puesto 6 motores (el baseline no compite, la regla lo excluye). 6 de los 33 aciertos lo son por SER el mejor de esos, o sea distancia 0,0000: mirar `ventaja_sobre_el_segundo_en_puntos` antes de leerlos como dominio. Puede perder 1 dataset(s) mas sin bajarse del liston. Sin medir: ninguna tarea; cubos todos cubiertos. lightgbm: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.531 puntos (maxima 3.361, en pc1); rango mediano 1.426 puntos y maximo 9.935. En 18 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.186, maxima 1.126 puntos." }, "matrixai.dense.torch_cpu": { "motor": "matrixai.dense.torch_cpu", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 5, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 16, "fraccion": 0.4, "cumple_la_regla": false, "aciertos_por_ser_el_mejor": 4, "datasets_que_puede_perder_sin_incumplir": null, "datasets_que_le_faltan_para_cumplir": 16, "definicion_de_mejor": "el motor con mejor media de los ajustes en ESE dataset, excluido el baseline dummy; un fallo (timeout/crash) cuenta como dataset perdido para ese motor", "detalle": [ { "dataset": "APSFailure", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.4976518958428433, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.4209167047357987, "alto": 0.5925481955230638, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Allstate_Claims_Severity", "cumple": false, "perdido_por_fallo": true, "mejor": "xgboost", "distancia_en_puntos": null, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Amazon_employee_access", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 18.808220360640792, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 18.046420729367256, "alto": 19.396015976437063, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Internet-Advertisements", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 1.487691437968952, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.2215798129304123, "alto": 1.7672037208346165, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "KDDCup09_appetency", "cumple": false, "perdido_por_fallo": true, "mejor": "catboost", "distancia_en_puntos": null, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Moneyball", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 6.230511611905431, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.307719927553025, "alto": 7.100208390219175, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "PhishingWebsites", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.47582298557116154, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.4208518460186659, "alto": 0.5352206601568319, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "SAT11-HAND-runtime-regression", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 6.381574417576086, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.2461082447706815, "alto": 7.426720807432666, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Satellite", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 0.0, "segundo": "sklearn.lineal", "ventaja_sobre_el_segundo_en_puntos": 1.6238805970149262, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "balance-scale", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 0.0, "segundo": "catboost", "ventaja_sobre_el_segundo_en_puntos": 4.550264550264549, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "banknote-authentication", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.05104975553636981, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.002876042565428764, "alto": 0.11396318665516086, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "climate-model-simulation-crashes", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.760942760942753, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.5710811821922959, "alto": 3.920688365132811, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "connect-4", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 4.137063351095316, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.4813499111900548, "alto": 4.837181764357605, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "diabetes", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.0234567901234448, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.24135802469135764, "alto": 1.960956790123454, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "diamonds", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.22525525475979302, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.12853863016702993, "alto": 0.3285456442602306, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "dresses-sales", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 2.639573070607537, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.3389436234263842, "alto": 4.982382320744391, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "house_16H", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 16.48329022695964, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 14.978723493519288, "alto": 17.559831111648588, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_prices_nominal", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 11.93748844863648, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 9.36650964061783, "alto": 14.631632940426163, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_sales", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 1.6278757081763962, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.828155971729981, "alto": 2.661488095591382, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "jm1", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 2.7503169767899016, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.431294942619535, "alto": 3.0549066474045787, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kc2", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.21419009370816644, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kick", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 2.3194505826168332, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.165844895038884, "alto": 2.479144756927285, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kr-vs-kp", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.07990576224600199, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.05468734661824132, "alto": 0.1093648768037697, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "letter", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 13.41995668832251, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 12.84661835748792, "alto": 13.956480093286691, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "mfeat-factors", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 2.9000000000000026, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.166666666666667, "alto": 3.616666666666666, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "micro-mass", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 27.079646017699112, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 24.955752212389378, "alto": 29.321533923303832, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "okcupid-stem", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.8962292015358817, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.4353155459289155, "alto": 2.3629024318204195, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "optdigits", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.8362989323843495, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.6227758007117412, "alto": 1.0735468564650055, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "ozone-level-8hr", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 2.4697368421052635, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.9920175438596495, "alto": 3.029912280701754, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc1", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 4.548004314994603, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.180690399136999, "alto": 7.670172599784249, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc3", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 4.618549822064056, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.871904655990511, "alto": 5.4397705367734295, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc4", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.4580439814814725, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.039171006944443, "alto": 2.8509295428240744, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pendigits", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.6033045323632025, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.4032135819311818, "alto": 0.8003638017280574, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "pol", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 1.348898063789683, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "sick", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 6.090027020388122, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.454735732416278, "alto": 6.764114058789815, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "splice", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 2.2048066875653194, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.9221525600835925, "alto": 2.4764890282131646, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "us_crime", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.4623198244844926, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.5482356216032624, "alto": 2.5314457049192836, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "wilt", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 3.202748852312176, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.7494366532303203, "alto": 3.668416190796104, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "wine_quality", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 5.616222992414633, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.044766466064045, "alto": 6.18053938426859, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "yeast", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.2651006711409405, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.5939597315436214, "alto": 2.9362416107382523, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 12, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null } ], "metrica_por_dataset": { "dresses-sales": "auroc", "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "diabetes": "auroc", "pc1": "auroc", "Moneyball": "r2", "banknote-authentication": "auroc", "pc4": "auroc", "house_prices_nominal": "r2", "yeast": "accuracy", "pc3": "auroc", "us_crime": "r2", "mfeat-factors": "accuracy", "ozone-level-8hr": "auroc", "splice": "accuracy", "kr-vs-kp": "auroc", "Internet-Advertisements": "auroc", "sick": "auroc", "SAT11-HAND-runtime-regression": "r2", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "kick": "auroc", "APSFailure": "auroc", "Allstate_Claims_Severity": "r2" }, "datasets_sin_metrica_declarada": [], "datasets_exigidos_que_no_se_midieron": [], "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`.", "dispersion": { "motor": "matrixai.dense.torch_cpu", "metrica": "auroc", "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "n_datasets": 38, "sd_mediana": 0.9797309110327281, "sd_maxima": 5.67503084011595, "rango_mediano": 2.9548987412266, "rango_maximo": 20.353982300884958, "dataset_mas_disperso": "micro-mass", "sd_entre_semillas_mediana": 0.512322422792107, "sd_entre_semillas_maxima": 3.2680657906691604, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 26, "cuales_con_rango_mayor_que_el_liston": [ "Amazon_employee_access", "Internet-Advertisements", "Moneyball", "SAT11-HAND-runtime-regression", "balance-scale", "climate-model-simulation-crashes", "connect-4", "diabetes", "dresses-sales", "house_16H", "house_prices_nominal", "house_sales", "kc2", "letter", "mfeat-factors", "micro-mass", "optdigits", "ozone-level-8hr", "pc1", "pc3", "pc4", "sick", "us_crime", "wilt", "wine_quality", "yeast" ], "por_dataset": { "APSFailure": { "n_medidas": 5, "media": 99.02586873762753, "sd": 0.09481920621888079, "rango": 0.2447144814984057, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Amazon_employee_access": { "n_medidas": 5, "media": 63.45756696949417, "sd": 0.9892793576915877, "rango": 2.6269409635948904, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Internet-Advertisements": { "n_medidas": 15, "media": 97.28209476821873, "sd": 0.6575537652038291, "rango": 2.015880357693476, "n_semillas": 3, "sd_entre_semillas": 0.028609652913392673, "rango_entre_semillas": 0.05704594511256289 }, "Moneyball": { "n_medidas": 15, "media": 87.12550960477546, "sd": 1.8945652513460822, "rango": 6.191314766526062, "n_semillas": 3, "sd_entre_semillas": 1.2930433947436402, "rango_entre_semillas": 2.5848146685731734 }, "PhishingWebsites": { "n_medidas": 15, "media": 98.96174781854253, "sd": 0.1128579411700658, "rango": 0.3962267278966891, "n_semillas": 3, "sd_entre_semillas": 0.06952665673755215, "rango_entre_semillas": 0.1326613504865719 }, "SAT11-HAND-runtime-regression": { "n_medidas": 15, "media": 68.80886360046178, "sd": 1.8653727120139776, "rango": 6.858733352281078, "n_semillas": 3, "sd_entre_semillas": 1.47081038697871, "rango_entre_semillas": 2.941434857055796 }, "Satellite": { "n_medidas": 15, "media": 99.18054173576562, "sd": 0.4968667794404345, "rango": 1.3333333333333286, "n_semillas": 3, "sd_entre_semillas": 0.31669615402594764, "rango_entre_semillas": 0.6116086235489178 }, "balance-scale": { "n_medidas": 15, "media": 94.60317460317461, "sd": 1.5644615247764477, "rango": 4.761904761904759, "n_semillas": 3, "sd_entre_semillas": 1.2598815766974303, "rango_entre_semillas": 2.380952380952394 }, "banknote-authentication": { "n_medidas": 15, "media": 99.93313201035376, "sd": 0.11694032104991091, "rango": 0.37208800690250143, "n_semillas": 3, "sd_entre_semillas": 0.088910542905632, "rango_entre_semillas": 0.16609145815358772 }, "climate-model-simulation-crashes": { "n_medidas": 15, "media": 93.23606434717546, "sd": 2.476945781878669, "rango": 8.080808080808097, "n_semillas": 3, "sd_entre_semillas": 1.096523252155588, "rango_entre_semillas": 1.9528619528619373 }, "connect-4": { "n_medidas": 5, "media": 76.42687981053878, "sd": 0.9156473476932102, "rango": 2.1684428656009374, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "diabetes": { "n_medidas": 15, "media": 78.91234567901235, "sd": 1.2952843114600492, "rango": 4.574074074074076, "n_semillas": 3, "sd_entre_semillas": 1.2488751225754477, "rango_entre_semillas": 2.177777777777777 }, "diamonds": { "n_medidas": 5, "media": 97.90516775223668, "sd": 0.11812669560882909, "rango": 0.3143629088983886, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "dresses-sales": { "n_medidas": 15, "media": 55.40503557744937, "sd": 5.443600658768519, "rango": 16.13300492610837, "n_semillas": 3, "sd_entre_semillas": 0.6181853488120675, "rango_entre_semillas": 1.1165845648604318 }, "house_16H": { "n_medidas": 5, "media": 49.74438338506431, "sd": 1.706345499491544, "rango": 4.157761671193562, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "house_prices_nominal": { "n_medidas": 15, "media": 79.6726989066178, "sd": 5.290637097462319, "rango": 18.9744500119168, "n_semillas": 3, "sd_entre_semillas": 1.448363065317815, "rango_entre_semillas": 2.7313881498959205 }, "house_sales": { "n_medidas": 5, "media": 85.33873148414803, "sd": 1.2886532285620624, "rango": 3.2738765052971104, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "jm1": { "n_medidas": 15, "media": 71.68466175014473, "sd": 0.21498526286544917, "rango": 0.8185035088383756, "n_semillas": 3, "sd_entre_semillas": 0.084889450629084, "rango_entre_semillas": 0.1636465947765089 }, "kc2": { "n_medidas": 15, "media": 91.54714094473131, "sd": 0.6927803465466965, "rango": 2.8686173264486428, "n_semillas": 3, "sd_entre_semillas": 0.292392654100079, "rango_entre_semillas": 0.5278255880665483 }, "kick": { "n_medidas": 5, "media": 74.28983068218687, "sd": 0.11696163988351663, "rango": 0.2849098444824705, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "kr-vs-kp": { "n_medidas": 15, "media": 99.90183567291645, "sd": 0.05481905462386521, "rango": 0.22479630902130054, "n_semillas": 3, "sd_entre_semillas": 0.01243087552235988, "rango_entre_semillas": 0.022185137920885722 }, "letter": { "n_medidas": 15, "media": 81.84574379476928, "sd": 1.0764891901706666, "rango": 3.348325837081461, "n_semillas": 3, "sd_entre_semillas": 0.4838121542646168, "rango_entre_semillas": 0.9545227386306863 }, "mfeat-factors": { "n_medidas": 15, "media": 94.95, "sd": 1.3235504200013366, "rango": 4.0, "n_semillas": 3, "sd_entre_semillas": 0.5408326913195972, "rango_entre_semillas": 1.0499999999999972 }, "micro-mass": { "n_medidas": 15, "media": 59.41002949852508, "sd": 5.67503084011595, "rango": 20.353982300884958, "n_semillas": 3, "sd_entre_semillas": 0.8730766127993346, "rango_entre_semillas": 1.5929203539823007 }, "okcupid-stem": { "n_medidas": 5, "media": 73.67332873880083, "sd": 0.5452002321142424, "rango": 1.142069508713206, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "optdigits": { "n_medidas": 15, "media": 98.03677342823251, "sd": 0.4970238852461649, "rango": 2.04626334519574, "n_semillas": 3, "sd_entre_semillas": 0.12872203092652468, "rango_entre_semillas": 0.23131672597865816 }, "ozone-level-8hr": { "n_medidas": 15, "media": 91.19517543859648, "sd": 0.8126899136768658, "rango": 2.901315789473685, "n_semillas": 3, "sd_entre_semillas": 0.2898213621802019, "rango_entre_semillas": 0.5039473684210378 }, "pc1": { "n_medidas": 15, "media": 76.51995685005393, "sd": 5.660112186446392, "rango": 19.935275080906152, "n_semillas": 3, "sd_entre_semillas": 3.2680657906691604, "rango_entre_semillas": 6.3430420711974165 }, "pc3": { "n_medidas": 15, "media": 76.42052194543298, "sd": 1.1890663144412463, "rango": 3.603202846975094, "n_semillas": 3, "sd_entre_semillas": 0.6327693872231331, "rango_entre_semillas": 1.263345195729542 }, "pc4": { "n_medidas": 15, "media": 91.13498263888889, "sd": 0.7447900045537749, "rango": 2.83203125, "n_semillas": 3, "sd_entre_semillas": 0.17481408310022553, "rango_entre_semillas": 0.3450520833333286 }, "pendigits": { "n_medidas": 15, "media": 98.30529028346218, "sd": 0.3452467355045525, "rango": 1.1368804001819086, "n_semillas": 3, "sd_entre_semillas": 0.13922646661458635, "rango_entre_semillas": 0.2455661664393034 }, "pol": { "n_medidas": 15, "media": 99.57464389169117, "sd": 0.0734699155700789, "rango": 0.2739759160212998, "n_semillas": 3, "sd_entre_semillas": 0.04022460645324819, "rango_entre_semillas": 0.07880611540529969 }, "sick": { "n_medidas": 15, "media": 93.36444771964301, "sd": 1.295636329246334, "rango": 4.507492016703509, "n_semillas": 3, "sd_entre_semillas": 0.8422460815490791, "rango_entre_semillas": 1.6617538688282991 }, "splice": { "n_medidas": 15, "media": 93.5005224660397, "sd": 0.5193968667043085, "rango": 1.7241379310344769, "n_semillas": 3, "sd_entre_semillas": 0.130511974888156, "rango_entre_semillas": 0.25078369905955356 }, "us_crime": { "n_medidas": 15, "media": 70.78072048843387, "sd": 1.4682804738674218, "rango": 5.429998434898977, "n_semillas": 3, "sd_entre_semillas": 1.2288850889818845, "rango_entre_semillas": 2.2225322707935646 }, "wilt": { "n_medidas": 15, "media": 96.56981301086104, "sd": 0.9701824643738687, "rango": 3.0084816929795153, "n_semillas": 3, "sd_entre_semillas": 0.6061784489378559, "rango_entre_semillas": 1.2042324487739364 }, "wine_quality": { "n_medidas": 15, "media": 37.46656828651441, "sd": 1.1975186164007434, "rango": 4.385608690621147, "n_semillas": 3, "sd_entre_semillas": 0.4356013187844437, "rango_entre_semillas": 0.8448625016621634 }, "yeast": { "n_medidas": 12, "media": 58.16554809843401, "sd": 1.282479066836426, "rango": 5.369127516778519, "n_semillas": 3, "sd_entre_semillas": 0.6832657563126746, "rango_entre_semillas": 1.2583892617449663 } }, "metrica_por_dataset": { "APSFailure": "auroc", "Amazon_employee_access": "auroc", "Internet-Advertisements": "auroc", "Moneyball": "r2", "PhishingWebsites": "auroc", "SAT11-HAND-runtime-regression": "r2", "Satellite": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "dresses-sales": "auroc", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "auroc", "kick": "auroc", "kr-vs-kp": "auroc", "letter": "accuracy", "mfeat-factors": "accuracy", "micro-mass": "accuracy", "okcupid-stem": "accuracy", "optdigits": "accuracy", "ozone-level-8hr": "auroc", "pc1": "auroc", "pc3": "auroc", "pc4": "auroc", "pendigits": "accuracy", "pol": "r2", "sick": "auroc", "splice": "accuracy", "us_crime": "r2", "wilt": "auroc", "wine_quality": "r2", "yeast": "accuracy" }, "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`. Y ojo al agregar: `rango_mediano`, `rango_maximo` y las desviaciones mezclan escalas de tareas distintas bajo una sola cifra.", "como_hay_que_leer_este_numero": "matrixai.dense.torch_cpu: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.980 puntos (maxima 5.675, en micro-mass); rango mediano 2.955 puntos y maximo 20.354. En 26 de 38 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.512, maxima 3.268 puntos." }, "como_hay_que_leer_este_numero": "Cada dataset se midio con la metrica de SU tarea (accuracy, auroc, r2), no con una sola para todos. Todos los datasets exigidos tienen medida. 16/40 sobre un ALCANCE RECORTADO: 7 de 7 motores del protocolo y 40 de 40 datasets. Configuraciones: 7 de 13 del protocolo (catboost: falta(n) 1; lightgbm: falta(n) 1; matrixai.dense.torch_cpu: falta(n) 1; sklearn.hgb: falta(n) 1; sklearn.logreg: falta(n) 1; xgboost: falta(n) 1); la regla mide «la mejor media de los AJUSTES» y aqui cada motor aporta 1 familia(s) de ajuste. NO corrieron: ninguno. Compitieron por el primer puesto 6 motores (el baseline no compite, la regla lo excluye). 4 de los 16 aciertos lo son por SER el mejor de esos, o sea distancia 0,0000: mirar `ventaja_sobre_el_segundo_en_puntos` antes de leerlos como dominio. NO cumple: le faltan 16 dataset(s) para llegar al liston. Sin medir: ninguna tarea; cubos todos cubiertos. matrixai.dense.torch_cpu: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.980 puntos (maxima 5.675, en micro-mass); rango mediano 2.955 puntos y maximo 20.354. En 26 de 38 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.512, maxima 3.268 puntos." }, "sklearn.hgb": { "motor": "sklearn.hgb", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 4, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 33, "fraccion": 0.825, "cumple_la_regla": true, "aciertos_por_ser_el_mejor": 9, "datasets_que_puede_perder_sin_incumplir": 1, "datasets_que_le_faltan_para_cumplir": null, "definicion_de_mejor": "el motor con mejor media de los ajustes en ESE dataset, excluido el baseline dummy; un fallo (timeout/crash) cuenta como dataset perdido para ese motor", "detalle": [ { "dataset": "APSFailure", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.32433927211816815, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.11746282929799949, "alto": 0.5997892492766854, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Allstate_Claims_Severity", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.570148270124804, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.34485402615652133, "alto": 0.8068812359991371, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Amazon_employee_access", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.31159266068532254, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.05424911515052422, "alto": 0.691075029081862, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Internet-Advertisements", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 1.2566810566348008, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.1273929746119822, "alto": 1.3738565114605805, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "KDDCup09_appetency", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 4.271453215551424, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.6647702703630265, "alto": 5.1569681911050385, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Moneyball", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "catboost", "ventaja_sobre_el_segundo_en_puntos": 0.03183270272505423, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "PhishingWebsites", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.0029841343523684394, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.0076842841116940095, "alto": 0.015042385760152667, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "SAT11-HAND-runtime-regression", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.550813683193363, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.21610404145662887, "alto": 0.8898620487644459, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Satellite", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.8918739635157444, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.0763515754560526, "alto": 2.7322277501382013, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "balance-scale", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 8.306878306878307, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 7.3015873015873005, "alto": 9.366402116402115, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "banknote-authentication", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.0032355478860934817, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "climate-model-simulation-crashes", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.4392068836513126, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.4141414141414144, "alto": 3.3524130190796866, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "connect-4", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 1.3647128478389448, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "diabetes", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 4.2641975308642, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.7801851851851804, "alto": 4.749475308641973, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "diamonds", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.06842424092415866, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.055994399701588815, "alto": 0.07735972624525633, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "dresses-sales", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 3.9778325123152602, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.7376163108921734, "alto": 6.003797208538589, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "house_16H", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.07165217152657055, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.2813742150448828, "alto": 0.42410394816977076, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_prices_nominal", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 1.3554220546628581, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.0110846031270153, "alto": 1.675617679466091, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_sales", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.16867634043601365, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "jm1", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.16944956957888335, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kc2", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 0.21419009370816644, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -1.1437177280550785, "alto": 1.6523235800344234, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kick", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "catboost", "ventaja_sobre_el_segundo_en_puntos": 0.021597807796625013, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kr-vs-kp", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.006413402702787341, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.0015035502764964585, "alto": 0.013023134059750122, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "letter", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.04497751124437066, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "mfeat-factors", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.20000000000000018, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.13333333333333275, "alto": 0.5170833333333352, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "micro-mass", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.11799410029498247, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "okcupid-stem", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.05316530471595193, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.08467067047356558, "alto": 0.17327951166683064, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "optdigits", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.10083036773429477, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "ozone-level-8hr", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.1907894736842053, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.24222587719298735, "alto": 0.6295065789473645, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc1", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 3.022653721682844, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.4170981661272923, "alto": 4.684412081984896, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc3", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.8540925266903865, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.3313037514827984, "alto": 1.3312852164887294, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc4", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.43764467592593004, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.14389829282407313, "alto": 0.7682291666666637, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pendigits", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.13642564802183177, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.09095043201455376, "alto": 0.18796422616340921, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "pol", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.3600338010916424, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.3081938408358833, "alto": 1.4110908474499022, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "sick", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.08924916073037581, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.20205416359616601, "alto": 0.4534256529927154, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "splice", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.1776384535005282, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.0522466039707418, "alto": 0.3030303030303005, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "us_crime", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.7233780776763066, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.13505097798634785, "alto": 1.5348270404489635, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "wilt", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0839771582129667, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.017495241294365915, "alto": 0.16070428843354595, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "wine_quality", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.1388375000623998, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.09488997450244846, "alto": 0.3913681294925445, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "yeast", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.796420581655479, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.7058165548098405, "alto": 3.8031319910514543, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 12, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null } ], "metrica_por_dataset": { "dresses-sales": "auroc", "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "diabetes": "auroc", "pc1": "auroc", "Moneyball": "r2", "banknote-authentication": "auroc", "pc4": "auroc", "house_prices_nominal": "r2", "yeast": "accuracy", "pc3": "auroc", "us_crime": "r2", "mfeat-factors": "accuracy", "ozone-level-8hr": "auroc", "splice": "accuracy", "kr-vs-kp": "auroc", "Internet-Advertisements": "auroc", "sick": "auroc", "SAT11-HAND-runtime-regression": "r2", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "kick": "auroc", "APSFailure": "auroc", "Allstate_Claims_Severity": "r2" }, "datasets_sin_metrica_declarada": [], "datasets_exigidos_que_no_se_midieron": [], "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`.", "dispersion": { "motor": "sklearn.hgb", "metrica": "auroc", "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "n_datasets": 40, "sd_mediana": 0.6154212327287277, "sd_maxima": 4.0997319060835755, "rango_mediano": 1.8876219608547729, "rango_maximo": 11.986863711001646, "dataset_mas_disperso": "dresses-sales", "sd_entre_semillas_mediana": 0.17283034463816374, "sd_entre_semillas_maxima": 2.0615775790779374, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 19, "cuales_con_rango_mayor_que_el_liston": [ "Amazon_employee_access", "SAT11-HAND-runtime-regression", "Satellite", "balance-scale", "climate-model-simulation-crashes", "diabetes", "dresses-sales", "house_16H", "house_prices_nominal", "jm1", "kc2", "micro-mass", "ozone-level-8hr", "pc1", "pc3", "pc4", "sick", "us_crime", "yeast" ], "por_dataset": { "APSFailure": { "n_medidas": 5, "media": 99.19918136135223, "sd": 0.3266670738625034, "rango": 0.8326724531749647, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Allstate_Claims_Severity": { "n_medidas": 5, "media": 56.24978587874707, "sd": 0.13749403358760573, "rango": 0.3216732167858609, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Amazon_employee_access": { "n_medidas": 5, "media": 81.95419466944963, "sd": 1.0671454469792612, "rango": 2.7091650832967957, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Internet-Advertisements": { "n_medidas": 15, "media": 97.51310514955289, "sd": 0.3754632223492047, "rango": 1.4357847671908672, "n_semillas": 3, "sd_entre_semillas": 0.0440640329484887, "rango_entre_semillas": 0.08711069996917331 }, "KDDCup09_appetency": { "n_medidas": 5, "media": 77.45301192690566, "sd": 0.5593709276363648, "rango": 1.441901807224781, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Moneyball": { "n_medidas": 15, "media": 93.35602121668089, "sd": 0.1771826372049924, "rango": 0.7204227402702799, "n_semillas": 3, "sd_entre_semillas": 0.08300992324798112, "rango_entre_semillas": 0.1625788863223363 }, "PhishingWebsites": { "n_medidas": 15, "media": 99.43458666976132, "sd": 0.038834019014119384, "rango": 0.15360002652563765, "n_semillas": 3, "sd_entre_semillas": 0.006438512355629118, "rango_entre_semillas": 0.01255823206618345 }, "SAT11-HAND-runtime-regression": { "n_medidas": 15, "media": 74.63962433484451, "sd": 0.7469856606123659, "rango": 2.924305719335706, "n_semillas": 3, "sd_entre_semillas": 0.2133731237230974, "rango_entre_semillas": 0.37492068429608594 }, "Satellite": { "n_medidas": 15, "media": 97.28866777224987, "sd": 1.51522303398836, "rango": 5.406301824212278, "n_semillas": 3, "sd_entre_semillas": 0.48574670548936216, "rango_entre_semillas": 0.9459369817578676 }, "balance-scale": { "n_medidas": 15, "media": 86.29629629629629, "sd": 1.4531355816414695, "rango": 4.761904761904759, "n_semillas": 3, "sd_entre_semillas": 0.557441997505438, "rango_entre_semillas": 1.1111111111111143 }, "banknote-authentication": { "n_medidas": 15, "media": 99.98418176589013, "sd": 0.007479586973478109, "rango": 0.032355478861092024, "n_semillas": 3, "sd_entre_semillas": 0.0022451100080507097, "rango_entre_semillas": 0.004314063848141814 }, "climate-model-simulation-crashes": { "n_medidas": 15, "media": 93.5578002244669, "sd": 2.435306907852384, "rango": 7.631874298540964, "n_semillas": 3, "sd_entre_semillas": 0.2375534286028836, "rango_entre_semillas": 0.4489337822671189 }, "connect-4": { "n_medidas": 5, "media": 80.5639431616341, "sd": 0.21724931207026651, "rango": 0.5846654825340494, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "diabetes": { "n_medidas": 15, "media": 75.67160493827161, "sd": 1.567859919743413, "rango": 5.629629629629633, "n_semillas": 3, "sd_entre_semillas": 0.4055907871274377, "rango_entre_semillas": 0.8111111111111171 }, "diamonds": { "n_medidas": 5, "media": 98.06199876607234, "sd": 0.02213761527980653, "rango": 0.05570462641502161, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "dresses-sales": { "n_medidas": 15, "media": 54.06677613574165, "sd": 3.714805939415729, "rango": 11.986863711001646, "n_semillas": 3, "sd_entre_semillas": 1.096061675775845, "rango_entre_semillas": 2.183908045977013 }, "house_16H": { "n_medidas": 5, "media": 66.15602144049738, "sd": 0.7771124598798301, "rango": 2.0582000752756215, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "house_prices_nominal": { "n_medidas": 15, "media": 90.2547653005914, "sd": 0.671632184639225, "rango": 2.5721465505212677, "n_semillas": 3, "sd_entre_semillas": 0.053190882405839766, "rango_entre_semillas": 0.105955152637776 }, "house_sales": { "n_medidas": 5, "media": 86.96660719232442, "sd": 0.6714715378210907, "rango": 1.8634150214072207, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "jm1": { "n_medidas": 15, "media": 74.43497872693463, "sd": 0.7301197439566179, "rango": 2.481684783490877, "n_semillas": 3, "sd_entre_semillas": 0.04497944134487681, "rango_entre_semillas": 0.0888301527440376 }, "kc2": { "n_medidas": 15, "media": 91.33295085102313, "sd": 2.6335511100423505, "rango": 8.318990246701105, "n_semillas": 3, "sd_entre_semillas": 0.7770794364038033, "rango_entre_semillas": 1.549053356282272 }, "kick": { "n_medidas": 5, "media": 76.6092812648037, "sd": 0.1443519502451187, "rango": 0.3669607984578249, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "kr-vs-kp": { "n_medidas": 15, "media": 99.97532803245967, "sd": 0.034686228235726285, "rango": 0.12270540885442927, "n_semillas": 3, "sd_entre_semillas": 0.005359928072893304, "rango_entre_semillas": 0.0098164327083623 }, "letter": { "n_medidas": 15, "media": 95.26570048309178, "sd": 0.20645588385580244, "rango": 0.9245377311344356, "n_semillas": 3, "sd_entre_semillas": 0.05962195624911597, "rango_entre_semillas": 0.11494252873562516 }, "mfeat-factors": { "n_medidas": 15, "media": 97.65, "sd": 0.48916839052182665, "rango": 1.5, "n_semillas": 3, "sd_entre_semillas": 0.13228756555323007, "rango_entre_semillas": 0.25 }, "micro-mass": { "n_medidas": 15, "media": 86.4896755162242, "sd": 2.5793444009585906, "rango": 7.079646017699105, "n_semillas": 3, "sd_entre_semillas": 1.1378938951024062, "rango_entre_semillas": 2.12389380530972 }, "okcupid-stem": { "n_medidas": 5, "media": 75.51639263562075, "sd": 0.10681267189221189, "rango": 0.24613566998128533, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "optdigits": { "n_medidas": 15, "media": 98.87307236061685, "sd": 0.23458512455358557, "rango": 0.9786476868327441, "n_semillas": 3, "sd_entre_semillas": 0.05719840309010827, "rango_entre_semillas": 0.10676156583629393 }, "ozone-level-8hr": { "n_medidas": 15, "media": 93.47412280701754, "sd": 0.9503219502505903, "rango": 3.4605263157894797, "n_semillas": 3, "sd_entre_semillas": 0.06990215427773702, "rango_entre_semillas": 0.12236842105262724 }, "pc1": { "n_medidas": 15, "media": 78.0453074433657, "sd": 4.0997319060835755, "rango": 11.521035598705495, "n_semillas": 3, "sd_entre_semillas": 2.0615775790779374, "rango_entre_semillas": 4.038834951456323 }, "pc3": { "n_medidas": 15, "media": 80.18497924080664, "sd": 1.6062854962228996, "rango": 5.48821174377224, "n_semillas": 3, "sd_entre_semillas": 0.47569057520797037, "rango_entre_semillas": 0.893016014234874 }, "pc4": { "n_medidas": 15, "media": 93.15538194444444, "sd": 0.8568143041110035, "rango": 3.1684027777777715, "n_semillas": 3, "sd_entre_semillas": 0.24648995027176682, "rango_entre_semillas": 0.48394097222224275 }, "pendigits": { "n_medidas": 15, "media": 98.77216916780354, "sd": 0.10733912584849181, "rango": 0.3638017280582204, "n_semillas": 3, "sd_entre_semillas": 0.047259230765866786, "rango_entre_semillas": 0.08185538881311061 }, "pol": { "n_medidas": 15, "media": 98.21461009059952, "sd": 0.0806870432941369, "rango": 0.34900195250014576, "n_semillas": 3, "sd_entre_semillas": 0.008435307008594475, "rango_entre_semillas": 0.015196461680147877 }, "sick": { "n_medidas": 15, "media": 99.36522557930076, "sd": 0.859483813209016, "rango": 3.432817489560307, "n_semillas": 3, "sd_entre_semillas": 0.26602523390863386, "rango_entre_semillas": 0.4820682878899589 }, "splice": { "n_medidas": 15, "media": 95.5276907001045, "sd": 0.29541945162894495, "rango": 1.0971786833855788, "n_semillas": 3, "sd_entre_semillas": 0.04788480350006035, "rango_entre_semillas": 0.09404388714733614 }, "us_crime": { "n_medidas": 15, "media": 71.51966223524208, "sd": 1.3683786247607197, "rango": 4.55229190801667, "n_semillas": 3, "sd_entre_semillas": 0.2236606372994641, "rango_entre_semillas": 0.420616219361861 }, "wilt": { "n_medidas": 15, "media": 99.68858470496025, "sd": 0.14236513898936962, "rango": 0.5101612361437731, "n_semillas": 3, "sd_entre_semillas": 0.035693585310473216, "rango_entre_semillas": 0.0692811555256867 }, "wine_quality": { "n_medidas": 15, "media": 42.94395377886665, "sd": 0.5585966516096235, "rango": 1.911828900302325, "n_semillas": 3, "sd_entre_semillas": 0.3396353084196454, "rango_entre_semillas": 0.6438214029811036 }, "yeast": { "n_medidas": 12, "media": 57.63422818791946, "sd": 1.424603448645745, "rango": 4.3624161073825505, "n_semillas": 3, "sd_entre_semillas": 0.5102988699914597, "rango_entre_semillas": 0.9228187919463053 } }, "metrica_por_dataset": { "APSFailure": "auroc", "Allstate_Claims_Severity": "r2", "Amazon_employee_access": "auroc", "Internet-Advertisements": "auroc", "KDDCup09_appetency": "auroc", "Moneyball": "r2", "PhishingWebsites": "auroc", "SAT11-HAND-runtime-regression": "r2", "Satellite": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "dresses-sales": "auroc", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "auroc", "kick": "auroc", "kr-vs-kp": "auroc", "letter": "accuracy", "mfeat-factors": "accuracy", "micro-mass": "accuracy", "okcupid-stem": "accuracy", "optdigits": "accuracy", "ozone-level-8hr": "auroc", "pc1": "auroc", "pc3": "auroc", "pc4": "auroc", "pendigits": "accuracy", "pol": "r2", "sick": "auroc", "splice": "accuracy", "us_crime": "r2", "wilt": "auroc", "wine_quality": "r2", "yeast": "accuracy" }, "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`. Y ojo al agregar: `rango_mediano`, `rango_maximo` y las desviaciones mezclan escalas de tareas distintas bajo una sola cifra.", "como_hay_que_leer_este_numero": "sklearn.hgb: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.615 puntos (maxima 4.100, en dresses-sales); rango mediano 1.888 puntos y maximo 11.987. En 19 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.173, maxima 2.062 puntos." }, "como_hay_que_leer_este_numero": "Cada dataset se midio con la metrica de SU tarea (accuracy, auroc, r2), no con una sola para todos. Todos los datasets exigidos tienen medida. 33/40 sobre un ALCANCE RECORTADO: 7 de 7 motores del protocolo y 40 de 40 datasets. Configuraciones: 7 de 13 del protocolo (catboost: falta(n) 1; lightgbm: falta(n) 1; matrixai.dense.torch_cpu: falta(n) 1; sklearn.hgb: falta(n) 1; sklearn.logreg: falta(n) 1; xgboost: falta(n) 1); la regla mide «la mejor media de los AJUSTES» y aqui cada motor aporta 1 familia(s) de ajuste. NO corrieron: ninguno. Compitieron por el primer puesto 6 motores (el baseline no compite, la regla lo excluye). 9 de los 33 aciertos lo son por SER el mejor de esos, o sea distancia 0,0000: mirar `ventaja_sobre_el_segundo_en_puntos` antes de leerlos como dominio. Puede perder 1 dataset(s) mas sin bajarse del liston. Sin medir: ninguna tarea; cubos todos cubiertos. sklearn.hgb: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.615 puntos (maxima 4.100, en dresses-sales); rango mediano 1.888 puntos y maximo 11.987. En 19 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.173, maxima 2.062 puntos." }, "xgboost": { "motor": "xgboost", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 5, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 29, "fraccion": 0.725, "cumple_la_regla": false, "aciertos_por_ser_el_mejor": 6, "datasets_que_puede_perder_sin_incumplir": null, "datasets_que_le_faltan_para_cumplir": 3, "definicion_de_mejor": "el motor con mejor media de los ajustes en ESE dataset, excluido el baseline dummy; un fallo (timeout/crash) cuenta como dataset perdido para ese motor", "detalle": [ { "dataset": "APSFailure", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.019703365311407595, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.06006273793208505, "alto": 0.08292858230546374, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Allstate_Claims_Severity", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.570148270124804, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Amazon_employee_access", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.43877080924090706, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.25490331828169843, "alto": 0.6325957949456984, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Internet-Advertisements", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 1.012565525747755, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.865379920855175, "alto": 1.1720821769966092, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "KDDCup09_appetency", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 6.53567776077093, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.8533125590568265, "alto": 7.611684615367014, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Moneyball", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.9024415227122384, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.7520750463565892, "alto": 1.049017328833307, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "PhishingWebsites", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.021035384096779186, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.012313422525794778, "alto": 0.031672372442070086, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "SAT11-HAND-runtime-regression", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.550813683193363, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Satellite", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 3.6263128800442246, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.376506357103372, "alto": 5.151276948590383, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "balance-scale", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 7.989417989417991, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 6.824074074074075, "alto": 9.25925925925926, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "banknote-authentication", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.012223180903070396, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.005392579810181708, "alto": 0.019422274949668833, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "climate-model-simulation-crashes", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.319491208380098, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.6010101010101006, "alto": 3.0753834642723548, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "connect-4", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 2.5414446417998615, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.403789224393129, "alto": 2.7072232089994053, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "diabetes", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.467901234567893, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.9332716049382712, "alto": 2.961975308641976, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "diamonds", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.04372417689785246, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "dresses-sales", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 5.680076628352482, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.696941707717569, "alto": 7.649493705528189, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "house_16H", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.3921126644648387, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.11676174933573424, "alto": 0.6759183275225791, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_prices_nominal", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.26973003626983294, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.055059141970387374, "alto": 0.6618939122288501, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_sales", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.7431484801808996, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.24022379654239484, "alto": 1.2199702933509045, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "jm1", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.2628111828329338, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.015064062677538608, "alto": 0.5151996683241459, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kc2", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 2.4803977816025946, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.0862019506597789, "alto": 4.05469497035762, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kick", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.3896322514845396, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.2010358261095102, "alto": 0.5926643526935482, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kr-vs-kp", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.018651222145871227, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.013675926834854989, "alto": 0.024017538693107077, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "letter", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 1.3726470098284094, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.2676578377477923, "alto": 1.4709312010661317, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "mfeat-factors", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.8666666666666711, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.46666666666666784, "alto": 1.2500000000000013, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "micro-mass", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 2.6548672566371723, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.4734513274336267, "alto": 3.893805309734513, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "okcupid-stem", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.10633060943191497, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.051294673624101304, "alto": 0.26976469429950134, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "optdigits", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.7413997627520819, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.5812574139976268, "alto": 0.9311981020166068, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "ozone-level-8hr", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.1907894736842053, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc1", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 3.0161812297734625, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.0882955771305274, "alto": 3.9159115426105697, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc3", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.3087930011862361, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.21586780842230305, "alto": 0.8676323398576533, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc4", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.8159722222222276, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.5692635995370351, "alto": 1.0611979166666667, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pendigits", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.4153403061997851, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.3243898741852357, "alto": 0.4881006518114302, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "pol", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.3696770742125275, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.3032516590817587, "alto": 1.4463707867022193, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "sick", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.23949889462049345, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.005455252599687903, "alto": 0.5375829034635238, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "splice", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.146290491118084, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "us_crime", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.6537213997567681, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.09164396956470615, "alto": 1.433840538412603, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "wilt", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.006718172657027566, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.036400599037064024, "alto": 0.05570834732952275, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "wine_quality", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.06532817634633536, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "yeast", "cumple": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 2.4888143176733823, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.5380313199105144, "alto": 3.495525727069348, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 12, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "catboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null } ], "metrica_por_dataset": { "dresses-sales": "auroc", "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "diabetes": "auroc", "pc1": "auroc", "Moneyball": "r2", "banknote-authentication": "auroc", "pc4": "auroc", "house_prices_nominal": "r2", "yeast": "accuracy", "pc3": "auroc", "us_crime": "r2", "mfeat-factors": "accuracy", "ozone-level-8hr": "auroc", "splice": "accuracy", "kr-vs-kp": "auroc", "Internet-Advertisements": "auroc", "sick": "auroc", "SAT11-HAND-runtime-regression": "r2", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "kick": "auroc", "APSFailure": "auroc", "Allstate_Claims_Severity": "r2" }, "datasets_sin_metrica_declarada": [], "datasets_exigidos_que_no_se_midieron": [], "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`.", "dispersion": { "motor": "xgboost", "metrica": "auroc", "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "n_datasets": 40, "sd_mediana": 0.7013018294646152, "sd_maxima": 4.008805130714038, "rango_mediano": 1.9472778042244343, "rango_maximo": 15.722495894909677, "dataset_mas_disperso": "dresses-sales", "sd_entre_semillas_mediana": 0.1081194440487879, "sd_entre_semillas_maxima": 0.9158814254376874, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 19, "cuales_con_rango_mayor_que_el_liston": [ "SAT11-HAND-runtime-regression", "Satellite", "balance-scale", "climate-model-simulation-crashes", "diabetes", "dresses-sales", "house_prices_nominal", "house_sales", "kc2", "mfeat-factors", "micro-mass", "ozone-level-8hr", "pc1", "pc3", "pc4", "sick", "us_crime", "wine_quality", "yeast" ], "por_dataset": { "APSFailure": { "n_medidas": 5, "media": 99.50381726815898, "sd": 0.0540657413073937, "rango": 0.1291792294807408, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Allstate_Claims_Severity": { "n_medidas": 5, "media": 56.819934148871866, "sd": 0.3559861241468916, "rango": 0.7270677958226557, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Amazon_employee_access": { "n_medidas": 5, "media": 81.82701652089408, "sd": 0.8080768573974971, "rango": 1.9434209165510765, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Internet-Advertisements": { "n_medidas": 15, "media": 97.75722068043993, "sd": 0.3466781803859666, "rango": 0.9568686401480164, "n_semillas": 3, "sd_entre_semillas": 0.100768834259649, "rango_entre_semillas": 0.2010098674067251 }, "KDDCup09_appetency": { "n_medidas": 5, "media": 75.18878738168614, "sd": 0.740169083579871, "rango": 1.951134691897792, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Moneyball": { "n_medidas": 15, "media": 92.45357969396863, "sd": 0.36709823564769434, "rango": 1.2657033494282501, "n_semillas": 3, "sd_entre_semillas": 0.04009325273503413, "rango_entre_semillas": 0.07893226396527098 }, "PhishingWebsites": { "n_medidas": 15, "media": 99.41653542001691, "sd": 0.024758070718300978, "rango": 0.08861221174092293, "n_semillas": 3, "sd_entre_semillas": 0.004524327544215859, "rango_entre_semillas": 0.008521361428407204 }, "SAT11-HAND-runtime-regression": { "n_medidas": 15, "media": 75.19043801803787, "sd": 0.7455180586994262, "rango": 2.405787334038507, "n_semillas": 3, "sd_entre_semillas": 0.042762267516158636, "rango_entre_semillas": 0.08543933085290689 }, "Satellite": { "n_medidas": 15, "media": 95.55422885572139, "sd": 2.806987000732529, "rango": 9.396351575456052, "n_semillas": 3, "sd_entre_semillas": 0.6530298386825535, "rango_entre_semillas": 1.286898839137649 }, "balance-scale": { "n_medidas": 15, "media": 86.6137566137566, "sd": 2.221952256574353, "rango": 7.936507936507937, "n_semillas": 3, "sd_entre_semillas": 0.7829972797327373, "rango_entre_semillas": 1.4285714285714306 }, "banknote-authentication": { "n_medidas": 15, "media": 99.97195858498705, "sd": 0.01719838070057127, "rango": 0.05931837791197836, "n_semillas": 3, "sd_entre_semillas": 0.005706970041118778, "rango_entre_semillas": 0.010785159620354534 }, "climate-model-simulation-crashes": { "n_medidas": 15, "media": 93.67751589973813, "sd": 2.0155946010991075, "rango": 7.744107744107751, "n_semillas": 3, "sd_entre_semillas": 0.6066145921050234, "rango_entre_semillas": 1.1447811447811347 }, "connect-4": { "n_medidas": 5, "media": 78.02249851983422, "sd": 0.13317379800799986, "rango": 0.35523978685613145, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "diabetes": { "n_medidas": 15, "media": 77.4679012345679, "sd": 1.465797713477048, "rango": 5.870370370370367, "n_semillas": 3, "sd_entre_semillas": 0.3927381514460734, "rango_entre_semillas": 0.7814814814814781 }, "diamonds": { "n_medidas": 5, "media": 98.13042300699647, "sd": 0.016639449262429103, "rango": 0.04300096918055374, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "dresses-sales": { "n_medidas": 15, "media": 52.364532019704434, "sd": 4.008805130714038, "rango": 15.722495894909677, "n_semillas": 3, "sd_entre_semillas": 0.8523179858305008, "rango_entre_semillas": 1.5681444991789917 }, "house_16H": { "n_medidas": 5, "media": 65.83556094755912, "sd": 0.6170370068861221, "rango": 1.560064445619588, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "house_prices_nominal": { "n_medidas": 15, "media": 91.34045731898443, "sd": 0.7876471578611042, "rango": 2.5597173836578264, "n_semillas": 3, "sd_entre_semillas": 0.179543766163126, "rango_entre_semillas": 0.33537004311303065 }, "house_sales": { "n_medidas": 5, "media": 86.22345871214351, "sd": 0.9207297041433105, "rango": 2.3729673705937273, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "jm1": { "n_medidas": 15, "media": 74.17216754410171, "sd": 0.3532996260718062, "rango": 1.1250872475232399, "n_semillas": 3, "sd_entre_semillas": 0.04015974859953531, "rango_entre_semillas": 0.07044730249594977 }, "kc2": { "n_medidas": 15, "media": 89.0667431631287, "sd": 2.902773189591613, "rango": 9.552495697074022, "n_semillas": 3, "sd_entre_semillas": 0.9158814254376874, "rango_entre_semillas": 1.589213998852557 }, "kick": { "n_medidas": 5, "media": 76.21964901331917, "sd": 0.14000032162517256, "rango": 0.31945841626667004, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "kr-vs-kp": { "n_medidas": 15, "media": 99.96309021301659, "sd": 0.027430362185485828, "rango": 0.10896240306273342, "n_semillas": 3, "sd_entre_semillas": 0.009024713799589678, "rango_entre_semillas": 0.016098949641687454 }, "letter": { "n_medidas": 15, "media": 93.89305347326336, "sd": 0.18381412484362566, "rango": 0.649675162418788, "n_semillas": 3, "sd_entre_semillas": 0.018018747003814024, "rango_entre_semillas": 0.03498250874561393 }, "mfeat-factors": { "n_medidas": 15, "media": 96.98333333333333, "sd": 0.6778818551368738, "rango": 2.25, "n_semillas": 3, "sd_entre_semillas": 0.1154700538379268, "rango_entre_semillas": 0.20000000000000284 }, "micro-mass": { "n_medidas": 15, "media": 83.83480825958704, "sd": 2.2553767250747367, "rango": 8.849557522123888, "n_semillas": 3, "sd_entre_semillas": 0.3684364600824964, "rango_entre_semillas": 0.707964601769902 }, "okcupid-stem": { "n_medidas": 5, "media": 75.4632273309048, "sd": 0.14758291488080005, "rango": 0.3741262183715719, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "optdigits": { "n_medidas": 15, "media": 98.13167259786478, "sd": 0.3100231549987433, "rango": 1.2455516014235002, "n_semillas": 3, "sd_entre_semillas": 0.030819409387349228, "rango_entre_semillas": 0.05338078291815407 }, "ozone-level-8hr": { "n_medidas": 15, "media": 93.66491228070176, "sd": 1.1281736077497375, "rango": 3.3881578947368496, "n_semillas": 3, "sd_entre_semillas": 0.14258164141666246, "rango_entre_semillas": 0.26578947368422234 }, "pc1": { "n_medidas": 15, "media": 78.05177993527508, "sd": 3.0054603415034378, "rango": 9.611650485436883, "n_semillas": 3, "sd_entre_semillas": 0.6395332972261956, "rango_entre_semillas": 1.2750809061488582 }, "pc3": { "n_medidas": 15, "media": 80.7302787663108, "sd": 1.2085920303468984, "rango": 4.02580071174377, "n_semillas": 3, "sd_entre_semillas": 0.6053871831378834, "rango_entre_semillas": 1.126556939501782 }, "pc4": { "n_medidas": 15, "media": 92.77705439814814, "sd": 0.7247218037923564, "rango": 2.5824652777777715, "n_semillas": 3, "sd_entre_semillas": 0.25508441833745676, "rango_entre_semillas": 0.5099826388888999 }, "pendigits": { "n_medidas": 15, "media": 98.49325450962559, "sd": 0.09549279755590472, "rango": 0.363801728058192, "n_semillas": 3, "sd_entre_semillas": 0.02923647343032855, "rango_entre_semillas": 0.05457025920873093 }, "pol": { "n_medidas": 15, "media": 98.20496681747863, "sd": 0.11254980579862194, "rango": 0.4263818650106259, "n_semillas": 3, "sd_entre_semillas": 0.03923489392297385, "rango_entre_semillas": 0.0784678914817789 }, "sick": { "n_medidas": 15, "media": 99.21497584541063, "sd": 0.7827394496238312, "rango": 3.165684107098997, "n_semillas": 3, "sd_entre_semillas": 0.23160625647028352, "rango_entre_semillas": 0.4206583149103551 }, "splice": { "n_medidas": 15, "media": 95.705329153605, "sd": 0.27018545685621675, "rango": 0.9404388714733471, "n_semillas": 3, "sd_entre_semillas": 0.08293891257255386, "rango_entre_semillas": 0.15673981191221742 }, "us_crime": { "n_medidas": 15, "media": 71.58931891316162, "sd": 1.264029213074131, "rango": 4.115740679705539, "n_semillas": 3, "sd_entre_semillas": 0.0987508730493722, "rango_entre_semillas": 0.19342548033685603 }, "wilt": { "n_medidas": 15, "media": 99.76584369051619, "sd": 0.09940703995034644, "rango": 0.3212126301645952, "n_semillas": 3, "sd_entre_semillas": 0.0035213709622207556, "rango_entre_semillas": 0.006298286865984437 }, "wine_quality": { "n_medidas": 15, "media": 43.082791278929044, "sd": 0.7705848682078539, "rango": 2.766787388342962, "n_semillas": 3, "sd_entre_semillas": 0.03812525005138763, "rango_entre_semillas": 0.07551267867113864 }, "yeast": { "n_medidas": 12, "media": 57.94183445190157, "sd": 1.8619740326616385, "rango": 5.369127516778519, "n_semillas": 3, "sd_entre_semillas": 0.2696770347033834, "rango_entre_semillas": 0.503355704697988 } }, "metrica_por_dataset": { "APSFailure": "auroc", "Allstate_Claims_Severity": "r2", "Amazon_employee_access": "auroc", "Internet-Advertisements": "auroc", "KDDCup09_appetency": "auroc", "Moneyball": "r2", "PhishingWebsites": "auroc", "SAT11-HAND-runtime-regression": "r2", "Satellite": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "dresses-sales": "auroc", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "auroc", "kick": "auroc", "kr-vs-kp": "auroc", "letter": "accuracy", "mfeat-factors": "accuracy", "micro-mass": "accuracy", "okcupid-stem": "accuracy", "optdigits": "accuracy", "ozone-level-8hr": "auroc", "pc1": "auroc", "pc3": "auroc", "pc4": "auroc", "pendigits": "accuracy", "pol": "r2", "sick": "auroc", "splice": "accuracy", "us_crime": "r2", "wilt": "auroc", "wine_quality": "r2", "yeast": "accuracy" }, "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`. Y ojo al agregar: `rango_mediano`, `rango_maximo` y las desviaciones mezclan escalas de tareas distintas bajo una sola cifra.", "como_hay_que_leer_este_numero": "xgboost: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.701 puntos (maxima 4.009, en dresses-sales); rango mediano 1.947 puntos y maximo 15.722. En 19 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.108, maxima 0.916 puntos." }, "como_hay_que_leer_este_numero": "Cada dataset se midio con la metrica de SU tarea (accuracy, auroc, r2), no con una sola para todos. Todos los datasets exigidos tienen medida. 29/40 sobre un ALCANCE RECORTADO: 7 de 7 motores del protocolo y 40 de 40 datasets. Configuraciones: 7 de 13 del protocolo (catboost: falta(n) 1; lightgbm: falta(n) 1; matrixai.dense.torch_cpu: falta(n) 1; sklearn.hgb: falta(n) 1; sklearn.logreg: falta(n) 1; xgboost: falta(n) 1); la regla mide «la mejor media de los AJUSTES» y aqui cada motor aporta 1 familia(s) de ajuste. NO corrieron: ninguno. Compitieron por el primer puesto 6 motores (el baseline no compite, la regla lo excluye). 6 de los 29 aciertos lo son por SER el mejor de esos, o sea distancia 0,0000: mirar `ventaja_sobre_el_segundo_en_puntos` antes de leerlos como dominio. NO cumple: le faltan 3 dataset(s) para llegar al liston. Sin medir: ninguna tarea; cubos todos cubiertos. xgboost: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.701 puntos (maxima 4.009, en dresses-sales); rango mediano 1.947 puntos y maximo 15.722. En 19 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.108, maxima 0.916 puntos." }, "catboost": { "motor": "catboost", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 2, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 29, "fraccion": 0.725, "cumple_la_regla": false, "aciertos_por_ser_el_mejor": 13, "datasets_que_puede_perder_sin_incumplir": null, "datasets_que_le_faltan_para_cumplir": 3, "definicion_de_mejor": "el motor con mejor media de los ajustes en ESE dataset, excluido el baseline dummy; un fallo (timeout/crash) cuenta como dataset perdido para ese motor", "detalle": [ { "dataset": "APSFailure", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "xgboost", "ventaja_sobre_el_segundo_en_puntos": 0.019703365311407595, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Allstate_Claims_Severity", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.7317067123485788, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.42828522605177444, "alto": 1.0460893014205097, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Amazon_employee_access", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 4.69943750838695, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 4.201889808568235, "alto": 5.196927621406644, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Internet-Advertisements", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.7710838729571345, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.5490318891972453, "alto": 1.0009604018912537, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "KDDCup09_appetency", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 4.271453215551424, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Moneyball", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.03183270272505423, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.14008355833741007, "alto": 0.18691088697095787, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "PhishingWebsites", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.018824914206139187, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.0004624717474336229, "alto": 0.03532172007714406, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "SAT11-HAND-runtime-regression", "cumple": false, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 14.237523407920838, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 13.782027899372132, "alto": 14.685925585683352, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "Satellite", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 4.523825317855168, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.4998949695964616, "alto": 5.63185185185185, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "balance-scale", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 4.550264550264549, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 3.597883597883597, "alto": 5.555555555555556, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "banknote-authentication", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.011504170261700253, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.003235547886109006, "alto": 0.02121081392004583, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "climate-model-simulation-crashes", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 1.0624766180321599, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "connect-4", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 4.6936056838365765, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 4.473060982830079, "alto": 4.890467732386026, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "diabetes", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "matrixai.dense.torch_cpu", "ventaja_sobre_el_segundo_en_puntos": 1.0234567901234448, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "diamonds", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 0.1875514699423153, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.16765036317554438, "alto": 0.21012904801838816, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "dresses-sales", "cumple": false, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 6.224685276409403, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 4.473145867542422, "alto": 7.8792761357416525, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "house_16H", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 1.6557716205487072, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.291181813579092, "alto": 2.059208672539332, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_prices_nominal", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "xgboost", "ventaja_sobre_el_segundo_en_puntos": 0.26973003626983294, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "house_sales", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 1.8799642769909197, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.426399853561362, "alto": 2.4245732712112122, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "jm1", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.757146180858026, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.4683675875676551, "alto": 1.032546482414327, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kc2", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.2296806272709904, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.09791547140944595, "alto": 2.692771084337351, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kick", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.021597807796625013, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.1894181099622511, "alto": 0.2660529437224102, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 5, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "kr-vs-kp", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 0.03658257255979569, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.02539020320015715, "alto": 0.04764896436634731, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "letter", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 11.019490254872565, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 10.851157754456102, "alto": 11.17774446110278, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "mfeat-factors", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 2.0166666666666666, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.733333333333334, "alto": 2.3166666666666687, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.lineal", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "micro-mass", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 7.079646017699115, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 5.722713864306784, "alto": 8.731563421828906, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "okcupid-stem", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.021659938958340508, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "optdigits", "cumple": false, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 2.1945432977461543, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.963078291814946, "alto": 2.4317912218268076, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "sklearn.hgb", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "ozone-level-8hr", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 1.9929824561403464, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.3735526315789446, "alto": 2.63815789473684, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": true, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc1", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 2.925566343042074, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc3", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "xgboost", "ventaja_sobre_el_segundo_en_puntos": 0.3087930011862361, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pc4", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "sklearn.hgb", "ventaja_sobre_el_segundo_en_puntos": 0.43764467592593004, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "pendigits", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 1.7795967864180806, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.634076095194786, "alto": 1.9402758829771103, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "lightgbm", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "pol", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 2.1420854076563023, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.1050830176985493, "alto": 2.177887062209382, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "matrixai.dense.torch_cpu", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "sick", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.014124293785311437, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "splice", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 1.2957157784744044, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.1180773249738725, "alto": 1.5047021943573629, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null }, { "dataset": "us_crime", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "xgboost", "ventaja_sobre_el_segundo_en_puntos": 0.6537213997567681, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "wilt", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "xgboost", "ventaja_sobre_el_segundo_en_puntos": 0.006718172657027566, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "wine_quality", "cumple": true, "perdido_por_fallo": false, "mejor": "xgboost", "distancia_en_puntos": 1.346875631753297, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.1045458671016901, "alto": 1.6174267830828026, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", "n_pliegues_emparejados": 15, "remuestreo": "bootstrap de percentiles, 1000 remuestras de PLIEGUES (no de filas: los registros crudos guardan la metrica ya agregada por pliegue), semilla 0", "contra": "xgboost", "cruza_el_liston": false, "lo_que_este_intervalo_NO_recoge": "el muestreo del conjunto de prueba. Los pliegues comparten UN test fijo por dataset y solo cambian los datos de entrenamiento, asi que esto mide la variabilidad del ENTRENAMIENTO y es mas estrecho que un intervalo sobre datos nuevos." }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "yeast", "cumple": true, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 0.0, "segundo": "matrixai.dense.torch_cpu", "ventaja_sobre_el_segundo_en_puntos": 2.2651006711409405, "intervalo_de_la_distancia": null, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null } ], "metrica_por_dataset": { "dresses-sales": "auroc", "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "diabetes": "auroc", "pc1": "auroc", "Moneyball": "r2", "banknote-authentication": "auroc", "pc4": "auroc", "house_prices_nominal": "r2", "yeast": "accuracy", "pc3": "auroc", "us_crime": "r2", "mfeat-factors": "accuracy", "ozone-level-8hr": "auroc", "splice": "accuracy", "kr-vs-kp": "auroc", "Internet-Advertisements": "auroc", "sick": "auroc", "SAT11-HAND-runtime-regression": "r2", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "kick": "auroc", "APSFailure": "auroc", "Allstate_Claims_Severity": "r2" }, "datasets_sin_metrica_declarada": [], "datasets_exigidos_que_no_se_midieron": [], "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`.", "dispersion": { "motor": "catboost", "metrica": "auroc", "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "n_datasets": 40, "sd_mediana": 0.43488134053498906, "sd_maxima": 3.1551514108882373, "rango_mediano": 1.3824438188007235, "rango_maximo": 9.688013136288994, "dataset_mas_disperso": "dresses-sales", "sd_entre_semillas_mediana": 0.1886662187410792, "sd_entre_semillas_maxima": 1.1603464985109617, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 16, "cuales_con_rango_mayor_que_el_liston": [ "SAT11-HAND-runtime-regression", "Satellite", "balance-scale", "climate-model-simulation-crashes", "diabetes", "dresses-sales", "house_prices_nominal", "house_sales", "kc2", "micro-mass", "ozone-level-8hr", "pc1", "pc3", "pc4", "us_crime", "yeast" ], "por_dataset": { "APSFailure": { "n_medidas": 5, "media": 99.52352063347038, "sd": 0.04503439722677709, "rango": 0.10913963758184764, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Allstate_Claims_Severity": { "n_medidas": 5, "media": 56.0882274365233, "sd": 0.13944478679902905, "rango": 0.30402044097269254, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Amazon_employee_access": { "n_medidas": 5, "media": 77.56634982174802, "sd": 0.3004654868817864, "rango": 0.7996979417473682, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Internet-Advertisements": { "n_medidas": 15, "media": 97.99870233323054, "sd": 0.5143148093865969, "rango": 1.7402867715078685, "n_semillas": 3, "sd_entre_semillas": 0.10859793698105032, "rango_entre_semillas": 0.2166204131976599 }, "KDDCup09_appetency": { "n_medidas": 5, "media": 81.72446514245709, "sd": 0.47817808753753854, "rango": 1.1273419679279897, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "Moneyball": { "n_medidas": 15, "media": 93.32418851395582, "sd": 0.31827021208057843, "rango": 1.0404626724542254, "n_semillas": 3, "sd_entre_semillas": 0.12356573920913508, "rango_entre_semillas": 0.24336672695727657 }, "PhishingWebsites": { "n_medidas": 15, "media": 99.41874588990754, "sd": 0.03357908887549482, "rango": 0.12578955221405863, "n_semillas": 3, "sd_entre_semillas": 0.00910650928714025, "rango_entre_semillas": 0.017258243671136597 }, "SAT11-HAND-runtime-regression": { "n_medidas": 15, "media": 60.95291461011701, "sd": 0.6495643735206047, "rango": 2.2424763806387844, "n_semillas": 3, "sd_entre_semillas": 0.18759243215534024, "rango_entre_semillas": 0.3286843293900077 }, "Satellite": { "n_medidas": 15, "media": 94.65671641791045, "sd": 2.0565303232356515, "rango": 7.217247097844108, "n_semillas": 3, "sd_entre_semillas": 0.7563029833100893, "rango_entre_semillas": 1.344610281923707 }, "balance-scale": { "n_medidas": 15, "media": 90.05291005291005, "sd": 1.1170342072206318, "rango": 3.9682539682539755, "n_semillas": 3, "sd_entre_semillas": 0.33042317451843145, "rango_entre_semillas": 0.6349206349206327 }, "banknote-authentication": { "n_medidas": 15, "media": 99.97267759562841, "sd": 0.017230557306254626, "rango": 0.07010353753236132, "n_semillas": 3, "sd_entre_semillas": 0.0073413063907992815, "rango_entre_semillas": 0.014020707506460894 }, "climate-model-simulation-crashes": { "n_medidas": 15, "media": 95.99700710811821, "sd": 0.9501253347699409, "rango": 2.8058361391694575, "n_semillas": 3, "sd_entre_semillas": 0.15766036293083058, "rango_entre_semillas": 0.3142536475869804 }, "connect-4": { "n_medidas": 5, "media": 75.8703374777975, "sd": 0.10859313682371186, "rango": 0.28863232682060413, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "diabetes": { "n_medidas": 15, "media": 79.9358024691358, "sd": 1.1335664478816452, "rango": 3.8703703703703667, "n_semillas": 3, "sd_entre_semillas": 0.5858684868288763, "rango_entre_semillas": 1.1037037037036868 }, "diamonds": { "n_medidas": 5, "media": 97.94287153705417, "sd": 0.015307836863715953, "rango": 0.04285548569696118, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "dresses-sales": { "n_medidas": 15, "media": 51.81992337164751, "sd": 3.1551514108882373, "rango": 9.688013136288994, "n_semillas": 3, "sd_entre_semillas": 0.7712910121333759, "rango_entre_semillas": 1.5270935960591174 }, "house_16H": { "n_medidas": 5, "media": 64.57190199147524, "sd": 0.8252348846821693, "rango": 1.9553608302316121, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "house_prices_nominal": { "n_medidas": 15, "media": 91.61018735525427, "sd": 0.8153977622921524, "rango": 2.9727400320031876, "n_semillas": 3, "sd_entre_semillas": 0.23462985864088476, "rango_entre_semillas": 0.41301508879847404 }, "house_sales": { "n_medidas": 5, "media": 85.0866429153335, "sd": 0.9568027083836447, "rango": 2.3465581352425318, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "jm1": { "n_medidas": 15, "media": 73.67783254607662, "sd": 0.34548863035299304, "rango": 1.3066838366185323, "n_semillas": 3, "sd_entre_semillas": 0.15362552116452988, "rango_entre_semillas": 0.2806529631693735 }, "kc2": { "n_medidas": 15, "media": 90.31746031746032, "sd": 2.728093787927081, "rango": 8.204245553643148, "n_semillas": 3, "sd_entre_semillas": 0.8069810252577442, "rango_entre_semillas": 1.4228341939185185 }, "kick": { "n_medidas": 5, "media": 76.58768345700707, "sd": 0.1453489283486085, "rango": 0.3925833614583212, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "kr-vs-kp": { "n_medidas": 15, "media": 99.94515886260267, "sd": 0.023950218274989877, "rango": 0.07264160204182701, "n_semillas": 3, "sd_entre_semillas": 0.006112519579244966, "rango_entre_semillas": 0.012172376558353903 }, "letter": { "n_medidas": 15, "media": 84.24621022821923, "sd": 0.37204981942761517, "rango": 1.1494252873563227, "n_semillas": 3, "sd_entre_semillas": 0.23217324445721185, "rango_entre_semillas": 0.4247876061969009 }, "mfeat-factors": { "n_medidas": 15, "media": 95.83333333333333, "sd": 0.43983221909312303, "rango": 1.5, "n_semillas": 3, "sd_entre_semillas": 0.2753785273643058, "rango_entre_semillas": 0.5 }, "micro-mass": { "n_medidas": 15, "media": 79.41002949852508, "sd": 2.0198567507091694, "rango": 5.309734513274336, "n_semillas": 3, "sd_entre_semillas": 0.6215724927936703, "rango_entre_semillas": 1.2389380530973426 }, "okcupid-stem": { "n_medidas": 5, "media": 75.56955794033671, "sd": 0.07955933386172272, "rango": 0.1772176823865408, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "optdigits": { "n_medidas": 15, "media": 96.6785290628707, "sd": 0.3945472922790641, "rango": 1.334519572953738, "n_semillas": 3, "sd_entre_semillas": 0.353342135435498, "rango_entre_semillas": 0.6939501779359318 }, "ozone-level-8hr": { "n_medidas": 15, "media": 91.6719298245614, "sd": 1.1291827079428582, "rango": 4.4671052631579045, "n_semillas": 3, "sd_entre_semillas": 0.18974000532681812, "rango_entre_semillas": 0.3460526315789423 }, "pc1": { "n_medidas": 15, "media": 81.06796116504853, "sd": 2.4994033254327714, "rango": 7.572815533980588, "n_semillas": 3, "sd_entre_semillas": 1.1603464985109617, "rango_entre_semillas": 2.0194174757281473 }, "pc3": { "n_medidas": 15, "media": 81.03907176749703, "sd": 1.293288463435193, "rango": 5.2491103202847, "n_semillas": 3, "sd_entre_semillas": 0.21550165017639364, "rango_entre_semillas": 0.3792259786476819 }, "pc4": { "n_medidas": 15, "media": 93.59302662037037, "sd": 0.5471017267738948, "rango": 2.05078125, "n_semillas": 3, "sd_entre_semillas": 0.15935874282905232, "rango_entre_semillas": 0.3125 }, "pendigits": { "n_medidas": 15, "media": 97.1289980294073, "sd": 0.2807843617515961, "rango": 0.818553888130964, "n_semillas": 3, "sd_entre_semillas": 0.17478896838441224, "rango_entre_semillas": 0.3456116416552959 }, "pol": { "n_medidas": 15, "media": 97.43255848403486, "sd": 0.057604005856034673, "rango": 0.2258703392053718, "n_semillas": 3, "sd_entre_semillas": 0.015306726178034854, "rango_entre_semillas": 0.03061273674468623 }, "sick": { "n_medidas": 15, "media": 99.45447474003112, "sd": 0.35521543663922034, "rango": 1.2988209285187793, "n_semillas": 3, "sd_entre_semillas": 0.06762443620491086, "rango_entre_semillas": 0.12404814541881137 }, "splice": { "n_medidas": 15, "media": 94.40961337513062, "sd": 0.42993046197685514, "rango": 1.4106583072100278, "n_semillas": 3, "sd_entre_semillas": 0.19153921400023832, "rango_entre_semillas": 0.37617554858933033 }, "us_crime": { "n_medidas": 15, "media": 72.24304031291838, "sd": 1.0788942314243426, "rango": 3.67034279121242, "n_semillas": 3, "sd_entre_semillas": 0.4561625530327146, "rango_entre_semillas": 0.9095667045597366 }, "wilt": { "n_medidas": 15, "media": 99.77256186317322, "sd": 0.05200767625614797, "rango": 0.18684917702385917, "n_semillas": 3, "sd_entre_semillas": 0.013240280250014199, "rango_entre_semillas": 0.02603291904603111 }, "wine_quality": { "n_medidas": 15, "media": 41.735915647175744, "sd": 0.38631178803024, "rango": 1.3542293303914192, "n_semillas": 3, "sd_entre_semillas": 0.028897680628074792, "rango_entre_semillas": 0.056048524371078656 }, "yeast": { "n_medidas": 12, "media": 60.43064876957495, "sd": 0.8399424391630078, "rango": 3.0201342281879207, "n_semillas": 3, "sd_entre_semillas": 0.09687085053517511, "rango_entre_semillas": 0.16778523489933406 } }, "metrica_por_dataset": { "APSFailure": "auroc", "Allstate_Claims_Severity": "r2", "Amazon_employee_access": "auroc", "Internet-Advertisements": "auroc", "KDDCup09_appetency": "auroc", "Moneyball": "r2", "PhishingWebsites": "auroc", "SAT11-HAND-runtime-regression": "r2", "Satellite": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "dresses-sales": "auroc", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "auroc", "kick": "auroc", "kr-vs-kp": "auroc", "letter": "accuracy", "mfeat-factors": "accuracy", "micro-mass": "accuracy", "okcupid-stem": "accuracy", "optdigits": "accuracy", "ozone-level-8hr": "auroc", "pc1": "auroc", "pc3": "auroc", "pc4": "auroc", "pendigits": "accuracy", "pol": "r2", "sick": "auroc", "splice": "accuracy", "us_crime": "r2", "wilt": "auroc", "wine_quality": "r2", "yeast": "accuracy" }, "la_metrica_de_arriba_no_gobierna": "hay un mapa por tarea: `metrica` es el valor por omision del parametro y NO es con lo que se midio. Lo que gobierna cada dataset esta en `metrica_por_dataset`. Y ojo al agregar: `rango_mediano`, `rango_maximo` y las desviaciones mezclan escalas de tareas distintas bajo una sola cifra.", "como_hay_que_leer_este_numero": "catboost: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.435 puntos (maxima 3.155, en dresses-sales); rango mediano 1.382 puntos y maximo 9.688. En 16 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.189, maxima 1.160 puntos." }, "como_hay_que_leer_este_numero": "Cada dataset se midio con la metrica de SU tarea (accuracy, auroc, r2), no con una sola para todos. Todos los datasets exigidos tienen medida. 29/40 sobre un ALCANCE RECORTADO: 7 de 7 motores del protocolo y 40 de 40 datasets. Configuraciones: 7 de 13 del protocolo (catboost: falta(n) 1; lightgbm: falta(n) 1; matrixai.dense.torch_cpu: falta(n) 1; sklearn.hgb: falta(n) 1; sklearn.logreg: falta(n) 1; xgboost: falta(n) 1); la regla mide «la mejor media de los AJUSTES» y aqui cada motor aporta 1 familia(s) de ajuste. NO corrieron: ninguno. Compitieron por el primer puesto 6 motores (el baseline no compite, la regla lo excluye). 13 de los 29 aciertos lo son por SER el mejor de esos, o sea distancia 0,0000: mirar `ventaja_sobre_el_segundo_en_puntos` antes de leerlos como dominio. NO cumple: le faltan 3 dataset(s) para llegar al liston. Sin medir: ninguna tarea; cubos todos cubiertos. catboost: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.435 puntos (maxima 3.155, en dresses-sales); rango mediano 1.382 puntos y maximo 9.688. En 16 de 40 datasets el rango supera los 2.0 puntos del liston entero de la regla: ahi la media sola NO describe al motor. Dispersion ENTRE SEMILLAS (la que el protocolo exige por su nombre): mediana 0.189, maxima 1.160 puntos." } } }, "digest_solo_de_resultados": "474071096d015d622b9b9d7357d1f38c5a4f097fe4865425ee7315fc54658066", "que_cubre_digest_resultados_crudos": "TODO el objeto de salida salvo este mismo campo —incluidos `alcance_y_veredicto`, el plan y la procedencia—, no solo `resultados`. El nombre es historico y NO se cambia: lo citan cinco artefactos y el `evidencia_digest` de la cartera aprobada. El digest de los resultados crudos a secas es `digest_solo_de_resultados`.", "digest_resultados_crudos": "600ebdaad2ca206eac148e25c38b57add8ad2d64652fafca3d8226758010c0d0" }