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"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 }, "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 }, "elevators": { "plan_digest": "09a0825634c637e0b5805f278bac071aa173c2f09f45618cc25ed7ca3d612b75", "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": 16599, "n_filas_leidas": 16599, "n_test": 3320, "n_predictores": 18 }, "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 }, "adult": { "plan_digest": "de53bba0b2a38bd9fb2c5909fe67006a01e242573b90ad6d2b069a789a3129a3", "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": 48842, "n_filas_leidas": 48842, "n_test": 9768, "n_predictores": 14 }, "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 }, "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." }, "total_wall_s": 61836.8, "n_intentos": 3479, "n_reusados": 0, "lectura_de_los_datos": { "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": [], "catalogo_leido": true }, "15": { "nombre": "breast-w", "objetivo": "Class", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "37": { "nombre": "diabetes", "objetivo": "class", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "1068": { "nombre": "pc1", "objetivo": "defects", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "41021": { "nombre": "Moneyball", "objetivo": "RS", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "1462": { "nombre": "banknote-authentication", "objetivo": "Class", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "1049": { "nombre": "pc4", "objetivo": "c", "normalizaciones": [], "columnas_excluidas": [], "motivos_de_exclusion": {}, "avisos": [], "catalogo_leido": true }, "42563": { "nombre": "house_prices_nominal", "objetivo": "SalePrice", "normalizaciones": [ "declaración nominal de 'MSZoning': comillas y espacios recortados", "declaración nominal de 'Street': comillas y espacios recortados", "declaración nominal de 'Alley': comillas y espacios recortados", "declaración nominal de 'LotShape': comillas y espacios recortados", "declaración nominal de 'LandContour': comillas y espacios recortados", "declaración nominal de 'Utilities': comillas y espacios recortados", "declaración nominal de 'LotConfig': comillas y espacios recortados", "declaración nominal de 'LandSlope': comillas y espacios recortados", "declaración nominal de 'Neighborhood': comillas y espacios recortados", "declaración nominal de 'Condition1': comillas y espacios recortados", "declaración nominal de 'Condition2': comillas y espacios recortados", "declaración nominal de 'BldgType': comillas y espacios recortados", "declaración nominal de 'HouseStyle': comillas y espacios recortados", "declaración nominal de 'RoofStyle': comillas y espacios recortados", "declaración nominal de 'RoofMatl': comillas y espacios recortados", "declaración nominal de 'Exterior1st': comillas y espacios recortados", "declaración nominal de 'Exterior2nd': comillas y espacios recortados", "declaración nominal de 'MasVnrType': comillas y espacios recortados", "declaración nominal de 'ExterQual': comillas y espacios recortados", "declaración nominal de 'ExterCond': comillas y espacios recortados", "declaración nominal de 'Foundation': comillas y espacios recortados", "declaración nominal de 'BsmtQual': comillas y espacios recortados", "declaración nominal de 'BsmtCond': comillas y espacios recortados", "declaración nominal de 'BsmtExposure': comillas y espacios recortados", "declaración nominal de 'BsmtFinType1': comillas y espacios recortados", "declaración nominal de 'BsmtFinType2': comillas y espacios recortados", "declaración nominal de 'Heating': comillas y espacios recortados", 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0.08959175095103088, "brier_score": 0.01738089763008805, "calibration_in_the_large": -0.005477900082091947, "sensitivity": 0.0056179775280898875, "specificity": 0.9994909387090206, "ppv": 0.16666666666666666, "npv": 0.9822893736241745, "accuracy": 0.9818, "macro_f1": 0.500843 }, "tiempo_de_ajuste": 104.60719559295103, "cpu_segundos": 109.73452431700001, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "193a1d504b379783", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "auroc": 0.7399835041262564, "average_precision": 0.07091427492962979, "pr_auc_trapezoidal": 0.06866756209077371, "log_loss": 0.08959175095103088, "brier_score": 0.01738089763008805, "calibration_in_the_large": -0.005477900082091947, "sensitivity": 0.0056179775280898875, "specificity": 0.9994909387090206, "ppv": 0.16666666666666666, "npv": 0.9822893736241745, "accuracy": 0.9818, "macro_f1": 0.500843 }, { "dataset": "KDDCup09_appetency", "data_id": 1111, "cubo": "grande", "tarea": "binary_classification", "sellado": false, "motor": "catboost", "repeticion": 0, "pliegue": 4, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "auroc", "wall_s": 214.567, "metricas": { "auroc": 0.8257423143184641, "average_precision": 0.13795472412775894, "pr_auc_trapezoidal": 0.13576386773554444, "log_loss": 0.07453629458488095, "brier_score": 0.016432165957037375, "calibration_in_the_large": 0.0003250509827562413, "sensitivity": 0.011235955056179775, "specificity": 0.9994909387090206, "ppv": 0.2857142857142857, "npv": 0.982387671369959, "accuracy": 0.9819, "macro_f1": 0.506244 }, "tiempo_de_ajuste": 108.79986063065007, "cpu_segundos": 122.311625222, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "a393530410a9756e", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "auroc": 0.8257423143184641, "average_precision": 0.13795472412775894, "pr_auc_trapezoidal": 0.13576386773554444, "log_loss": 0.07453629458488095, "brier_score": 0.016432165957037375, "calibration_in_the_large": 0.0003250509827562413, "sensitivity": 0.011235955056179775, "specificity": 0.9994909387090206, "ppv": 0.2857142857142857, "npv": 0.982387671369959, "accuracy": 0.9819, "macro_f1": 0.506244 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "baseline", "repeticion": 0, "pliegue": 0, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 6.422, "metricas": { "log_loss": 0.7787505904340233, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, "tiempo_de_ajuste": 0.007244918029755354, "cpu_segundos": 0.005409600000000125, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "14668a59e959a0d3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.7787505904340233, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.lineal", "repeticion": 0, "pliegue": 0, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 17.671, "metricas": { "log_loss": 0.6222822328592198, "accuracy": 0.7381116471399035, "macro_f1": 0.502024 }, "tiempo_de_ajuste": 10.881637307349592, "cpu_segundos": 65.46944361700001, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "b3bb6150f18ca140", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.6222822328592198, "accuracy": 0.7381116471399035, "macro_f1": 0.502024 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "lightgbm", "repeticion": 0, "pliegue": 0, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 13.621, "metricas": { "log_loss": 0.5716408829258374, "accuracy": 0.755144235502609, "macro_f1": 0.585447 }, "tiempo_de_ajuste": 4.9232357731089, "cpu_segundos": 6.843560077, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "4a80d10e82ba81bc", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5716408829258374, "accuracy": 0.755144235502609, "macro_f1": 0.585447 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "matrixai.dense.torch_cpu", "repeticion": 0, "pliegue": 0, "estado": "failed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 5.487, "metricas": {}, "tiempo_de_ajuste": null, "cpu_segundos": null, "motivo": "el subproceso aislado capturó una excepción no declarada: DatasetProjectError: El valor 'native american, white, other' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.", "traza": "Traceback (most recent call last):\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/subproceso.py\", line 151, in _entrada_del_proceso_hijo\n intento = ejecutar_intento(motor, train, validation, test, spec, presupuesto,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/harness.py\", line 183, in ejecutar_intento\n fit_result, fitted = motor.fit(train, validation, spec, presupuesto, candidate=candidate,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motor.py\", line 465, in fit\n resultado = self._ajustar(train, validation, spec, budget, semilla=rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motores/densa.py\", line 408, in _ajustar\n resultado_generacion = generate_project_from_dataset(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 756, in generate_project_from_dataset\n _check_categorical_values_safe(values, col)\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 2080, in _check_categorical_values_safe\n raise DatasetProjectError(\nmatrixai.training.dataset_project.DatasetProjectError: El valor 'native american, white, other' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.\n", "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "d579b3d111381693", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.hgb", "repeticion": 0, "pliegue": 0, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 14.98, "metricas": { "log_loss": 0.5757876986142894, "accuracy": 0.7554395983065866, "macro_f1": 0.578009 }, "tiempo_de_ajuste": 6.3578439499251544, "cpu_segundos": 9.896523066, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "abcc06d146e8fec3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5757876986142894, "accuracy": 0.7554395983065866, "macro_f1": 0.578009 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "xgboost", "repeticion": 0, "pliegue": 0, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 14.746, "metricas": { "log_loss": 0.5750925954541259, "accuracy": 0.7559318696465491, "macro_f1": 0.574279 }, "tiempo_de_ajuste": 5.635869635269046, "cpu_segundos": 9.204543955, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "193a1d504b379783", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5750925954541259, "accuracy": 0.7559318696465491, "macro_f1": 0.574279 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "catboost", "repeticion": 0, "pliegue": 0, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 19.704, "metricas": { "log_loss": 0.5769376000396672, "accuracy": 0.7548488726986314, "macro_f1": 0.55674 }, "tiempo_de_ajuste": 11.1322789369151, "cpu_segundos": 29.919347168999998, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "a393530410a9756e", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5769376000396672, "accuracy": 0.7548488726986314, "macro_f1": 0.55674 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "baseline", "repeticion": 0, "pliegue": 1, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 6.583, "metricas": { "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, "tiempo_de_ajuste": 0.007305060978978872, "cpu_segundos": 0.005575828000000005, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "14668a59e959a0d3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.lineal", "repeticion": 0, "pliegue": 1, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 18.128, "metricas": { "log_loss": 0.6454417513460524, "accuracy": 0.7326966624003151, "macro_f1": 0.510118 }, "tiempo_de_ajuste": 11.011166310869157, "cpu_segundos": 66.53936028, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "b3bb6150f18ca140", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.6454417513460524, "accuracy": 0.7326966624003151, "macro_f1": 0.510118 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "lightgbm", "repeticion": 0, "pliegue": 1, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 13.89, "metricas": { "log_loss": 0.5719819696604257, "accuracy": 0.7552426897706016, "macro_f1": 0.585699 }, "tiempo_de_ajuste": 4.734751112759113, "cpu_segundos": 6.202268817999999, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "4a80d10e82ba81bc", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5719819696604257, "accuracy": 0.7552426897706016, "macro_f1": 0.585699 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "matrixai.dense.torch_cpu", "repeticion": 0, "pliegue": 1, "estado": "failed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 5.837, "metricas": {}, "tiempo_de_ajuste": null, "cpu_segundos": null, "motivo": "el subproceso aislado capturó una excepción no declarada: DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.", "traza": "Traceback (most recent call last):\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/subproceso.py\", line 151, in _entrada_del_proceso_hijo\n intento = ejecutar_intento(motor, train, validation, test, spec, presupuesto,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/harness.py\", line 183, in ejecutar_intento\n fit_result, fitted = motor.fit(train, validation, spec, presupuesto, candidate=candidate,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motor.py\", line 465, in fit\n resultado = self._ajustar(train, validation, spec, budget, semilla=rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motores/densa.py\", line 408, in _ajustar\n resultado_generacion = generate_project_from_dataset(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 756, in generate_project_from_dataset\n _check_categorical_values_safe(values, col)\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 2080, in _check_categorical_values_safe\n raise DatasetProjectError(\nmatrixai.training.dataset_project.DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.\n", "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "d579b3d111381693", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.hgb", "repeticion": 0, "pliegue": 1, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 15.924, "metricas": { "log_loss": 0.5732916516289002, "accuracy": 0.7564241409865118, "macro_f1": 0.581982 }, "tiempo_de_ajuste": 6.600142616778612, "cpu_segundos": 10.269019592, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "abcc06d146e8fec3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5732916516289002, "accuracy": 0.7564241409865118, "macro_f1": 0.581982 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "xgboost", "repeticion": 0, "pliegue": 1, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 14.147, "metricas": { "log_loss": 0.5724164095239833, "accuracy": 0.7556365068425717, "macro_f1": 0.570065 }, "tiempo_de_ajuste": 5.025681745726615, "cpu_segundos": 8.378280200999999, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "193a1d504b379783", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5724164095239833, "accuracy": 0.7556365068425717, "macro_f1": 0.570065 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "catboost", "repeticion": 0, "pliegue": 1, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 18.9, "metricas": { "log_loss": 0.5781907900034442, "accuracy": 0.754947326966624, "macro_f1": 0.550335 }, "tiempo_de_ajuste": 10.532035689800978, "cpu_segundos": 28.479093919, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "a393530410a9756e", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5781907900034442, "accuracy": 0.754947326966624, "macro_f1": 0.550335 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "baseline", "repeticion": 0, "pliegue": 2, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 6.048, "metricas": { "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, "tiempo_de_ajuste": 0.007798247039318085, "cpu_segundos": 0.006105642999999938, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "14668a59e959a0d3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.lineal", "repeticion": 0, "pliegue": 2, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 17.336, "metricas": { "log_loss": 0.6212922818506492, "accuracy": 0.7379147386039184, "macro_f1": 0.499954 }, "tiempo_de_ajuste": 10.671520513948053, "cpu_segundos": 63.877546925000004, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "b3bb6150f18ca140", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.6212922818506492, "accuracy": 0.7379147386039184, "macro_f1": 0.499954 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "lightgbm", "repeticion": 0, "pliegue": 2, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 13.49, "metricas": { "log_loss": 0.5721105055856963, "accuracy": 0.7532736044107512, "macro_f1": 0.578468 }, "tiempo_de_ajuste": 4.775570949073881, "cpu_segundos": 6.467303252000001, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "4a80d10e82ba81bc", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5721105055856963, "accuracy": 0.7532736044107512, "macro_f1": 0.578468 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "matrixai.dense.torch_cpu", "repeticion": 0, "pliegue": 2, "estado": "failed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 5.568, "metricas": {}, "tiempo_de_ajuste": null, "cpu_segundos": null, "motivo": "el subproceso aislado capturó una excepción no declarada: DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.", "traza": "Traceback (most recent call last):\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/subproceso.py\", line 151, in _entrada_del_proceso_hijo\n intento = ejecutar_intento(motor, train, validation, test, spec, presupuesto,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/harness.py\", line 183, in ejecutar_intento\n fit_result, fitted = motor.fit(train, validation, spec, presupuesto, candidate=candidate,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motor.py\", line 465, in fit\n resultado = self._ajustar(train, validation, spec, budget, semilla=rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motores/densa.py\", line 408, in _ajustar\n resultado_generacion = generate_project_from_dataset(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 756, in generate_project_from_dataset\n _check_categorical_values_safe(values, col)\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 2080, in _check_categorical_values_safe\n raise DatasetProjectError(\nmatrixai.training.dataset_project.DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.\n", "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "d579b3d111381693", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.hgb", "repeticion": 0, "pliegue": 2, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 15.339, "metricas": { "log_loss": 0.5733028994024526, "accuracy": 0.7539627842866988, "macro_f1": 0.578123 }, "tiempo_de_ajuste": 6.039608071092516, "cpu_segundos": 9.602480442000001, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "abcc06d146e8fec3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5733028994024526, "accuracy": 0.7539627842866988, "macro_f1": 0.578123 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "xgboost", "repeticion": 0, "pliegue": 2, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 14.957, "metricas": { "log_loss": 0.5725953257265248, "accuracy": 0.7521906074628335, "macro_f1": 0.565946 }, "tiempo_de_ajuste": 5.4146070457063615, "cpu_segundos": 9.103733672, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "193a1d504b379783", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5725953257265248, "accuracy": 0.7521906074628335, "macro_f1": 0.565946 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "catboost", "repeticion": 0, "pliegue": 2, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 20.362, "metricas": { "log_loss": 0.5759729891048752, "accuracy": 0.7563256867185193, "macro_f1": 0.553914 }, "tiempo_de_ajuste": 11.607529947068542, "cpu_segundos": 30.206009057, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "a393530410a9756e", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5759729891048752, "accuracy": 0.7563256867185193, "macro_f1": 0.553914 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "baseline", "repeticion": 0, "pliegue": 3, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 6.47, "metricas": { "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, "tiempo_de_ajuste": 0.010001244023442268, "cpu_segundos": 0.00784724300000006, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "14668a59e959a0d3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.lineal", "repeticion": 0, "pliegue": 3, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 17.696, "metricas": { "log_loss": 0.6205220990288522, "accuracy": 0.7373240129959634, "macro_f1": 0.490341 }, "tiempo_de_ajuste": 10.921380589716136, "cpu_segundos": 66.216525416, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "b3bb6150f18ca140", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.6205220990288522, "accuracy": 0.7373240129959634, "macro_f1": 0.490341 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "lightgbm", "repeticion": 0, "pliegue": 3, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 14.138, "metricas": { "log_loss": 0.571020656020088, "accuracy": 0.7571133208624594, "macro_f1": 0.584589 }, "tiempo_de_ajuste": 4.6707344627939165, "cpu_segundos": 6.222383391, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "4a80d10e82ba81bc", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.571020656020088, "accuracy": 0.7571133208624594, "macro_f1": 0.584589 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "matrixai.dense.torch_cpu", "repeticion": 0, "pliegue": 3, "estado": "failed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 5.298, "metricas": {}, "tiempo_de_ajuste": null, "cpu_segundos": null, "motivo": "el subproceso aislado capturó una excepción no declarada: DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.", "traza": "Traceback (most recent call last):\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/subproceso.py\", line 151, in _entrada_del_proceso_hijo\n intento = ejecutar_intento(motor, train, validation, test, spec, presupuesto,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/harness.py\", line 183, in ejecutar_intento\n fit_result, fitted = motor.fit(train, validation, spec, presupuesto, candidate=candidate,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motor.py\", line 465, in fit\n resultado = self._ajustar(train, validation, spec, budget, semilla=rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motores/densa.py\", line 408, in _ajustar\n resultado_generacion = generate_project_from_dataset(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 756, in generate_project_from_dataset\n _check_categorical_values_safe(values, col)\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 2080, in _check_categorical_values_safe\n raise DatasetProjectError(\nmatrixai.training.dataset_project.DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.\n", "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "d579b3d111381693", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.hgb", "repeticion": 0, "pliegue": 3, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 14.821, "metricas": { "log_loss": 0.5746820420914072, "accuracy": 0.7541596928226839, "macro_f1": 0.573938 }, "tiempo_de_ajuste": 6.204074981156737, "cpu_segundos": 9.380098787, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "abcc06d146e8fec3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5746820420914072, "accuracy": 0.7541596928226839, "macro_f1": 0.573938 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "xgboost", "repeticion": 0, "pliegue": 3, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 13.538, "metricas": { "log_loss": 0.5738752022309346, "accuracy": 0.754553509894654, "macro_f1": 0.570202 }, "tiempo_de_ajuste": 4.981091664172709, "cpu_segundos": 8.227576761, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "193a1d504b379783", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5738752022309346, "accuracy": 0.754553509894654, "macro_f1": 0.570202 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "catboost", "repeticion": 0, "pliegue": 3, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 19.528, "metricas": { "log_loss": 0.5775485347393395, "accuracy": 0.7557349611105642, "macro_f1": 0.549988 }, "tiempo_de_ajuste": 10.909045136999339, "cpu_segundos": 29.291190013, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "a393530410a9756e", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5775485347393395, "accuracy": 0.7557349611105642, "macro_f1": 0.549988 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "baseline", "repeticion": 0, "pliegue": 4, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 6.626, "metricas": { "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, "tiempo_de_ajuste": 0.011113513726741076, "cpu_segundos": 0.008954397999999975, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "14668a59e959a0d3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.7787506048521038, "accuracy": 0.715762528305602, "macro_f1": 0.278113 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.lineal", "repeticion": 0, "pliegue": 4, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 17.124, "metricas": { "log_loss": 0.6228153219559815, "accuracy": 0.738013192871911, "macro_f1": 0.50804 }, "tiempo_de_ajuste": 10.721514751203358, "cpu_segundos": 64.610107955, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "b3bb6150f18ca140", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.6228153219559815, "accuracy": 0.738013192871911, "macro_f1": 0.50804 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "lightgbm", "repeticion": 0, "pliegue": 4, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 13.219, "metricas": { "log_loss": 0.5703283290097375, "accuracy": 0.7566210495224968, "macro_f1": 0.592576 }, "tiempo_de_ajuste": 4.645771089009941, "cpu_segundos": 6.079797802, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "4a80d10e82ba81bc", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5703283290097375, "accuracy": 0.7566210495224968, "macro_f1": 0.592576 }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "matrixai.dense.torch_cpu", "repeticion": 0, "pliegue": 4, "estado": "failed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 5.9, "metricas": {}, "tiempo_de_ajuste": null, "cpu_segundos": null, "motivo": "el subproceso aislado capturó una excepción no declarada: DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.", "traza": "Traceback (most recent call last):\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/subproceso.py\", line 151, in _entrada_del_proceso_hijo\n intento = ejecutar_intento(motor, train, validation, test, spec, presupuesto,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/harness.py\", line 183, in ejecutar_intento\n fit_result, fitted = motor.fit(train, validation, spec, presupuesto, candidate=candidate,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motor.py\", line 465, in fit\n resultado = self._ajustar(train, validation, spec, budget, semilla=rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixai-engines/src/matrixai_engines/motores/densa.py\", line 408, in _ajustar\n resultado_generacion = generate_project_from_dataset(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 756, in generate_project_from_dataset\n _check_categorical_values_safe(values, col)\n File \"/home/deployer/matrixAI/matrixai/training/dataset_project.py\", line 2080, in _check_categorical_values_safe\n raise DatasetProjectError(\nmatrixai.training.dataset_project.DatasetProjectError: El valor 'asian, pacific islander' de la columna categórica 'ethnicity' contiene una coma, ']' o un salto de línea — el prompt tipado no puede representarlo sin ambigüedad. Limpia ese valor en el CSV de origen antes de generar el modelo.\n", "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "d579b3d111381693", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false }, { "dataset": "okcupid-stem", "data_id": 42734, "cubo": "grande", "tarea": "multiclass_classification", "sellado": false, "motor": "sklearn.hgb", "repeticion": 0, "pliegue": 4, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "accuracy", "wall_s": 15.207, "metricas": { "log_loss": 0.5736304767918248, "accuracy": 0.7558334153785566, "macro_f1": 0.582701 }, "tiempo_de_ajuste": 6.184759391006082, "cpu_segundos": 9.679747643999999, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "abcc06d146e8fec3", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "log_loss": 0.5736304767918248, "accuracy": 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1882.0034435608454, "r2": 0.5714723787213006 }, { "dataset": "Allstate_Claims_Severity", "data_id": 42571, "cubo": "grande", "tarea": "regression", "sellado": false, "motor": "catboost", "repeticion": 0, "pliegue": 4, "estado": "completed", "semilla": 0, "presupuesto_wall_s": 600.0, "configuracion": "default", "metrica_de_cierre": "r2", "wall_s": 70.603, "metricas": { "mae": 1223.335227916108, "rmse": 1907.7745712312446, "r2": 0.5596559786344713 }, "tiempo_de_ajuste": 49.79752719402313, "cpu_segundos": 68.53995575399999, "motivo": null, "traza": null, "entorno_digest": "e55a65b30bf7d59f", "motor_digest": "a393530410a9756e", "procedencia_id": "c70557088d7982d65d51399d786696251ce82c822d0a346fe562fc9b7da29212", "reusado": false, "mae": 1223.335227916108, "rmse": 1907.7745712312446, "r2": 0.5596559786344713 } ], "alcance_y_veredicto": { "alcance": { "protocolo": { "version": "101-C1.v1", "fecha_registro": "2026-09-06", "digest_sha256": "ea50ca482a815627364b3439bfcf10e78295a13ae5f821219d0fb40329e65301", "n_datasets": 40, "n_motores": 7 }, "motores": { "del_protocolo": [ "dummy", "sklearn.logreg", "sklearn.hgb", "lightgbm", "xgboost", "catboost", "matrixai.dense.torch_cpu" ], "declarados_por_la_pasada": [ "baseline", "sklearn.lineal", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "xgboost", "catboost" ], "observados_en_los_resultados": [ "baseline", "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "equivalencias_de_nombre": { "baseline": "dummy", "sklearn.lineal": "sklearn.logreg" }, "que_faltan": [], "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", "breast-w", "optdigits", "pendigits", "diabetes", "sick", "splice", "yeast", "pol", "elevators", "wine_quality", "house_16H", "pc4", "pc3", "jm1", "kc2", "pc1", "KDDCup09_appetency", "banknote-authentication", "ozone-level-8hr", "micro-mass", "adult", "Amazon_employee_access", "PhishingWebsites", "connect-4", "Satellite", "Internet-Advertisements", "wilt", "climate-model-simulation-crashes", "Moneyball", "APSFailure", "diamonds", "house_prices_nominal", "Allstate_Claims_Severity", "us_crime", "house_sales", "okcupid-stem" ], "declarados_por_la_pasada": [ "kc2", "climate-model-simulation-crashes", "micro-mass", "balance-scale", "breast-w", "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", "wilt", "Satellite", "optdigits", "wine_quality", "jm1", "pendigits", "PhishingWebsites", "pol", "elevators", "letter", "house_sales", "house_16H", "Amazon_employee_access", "adult", "KDDCup09_appetency", "okcupid-stem", "diamonds", "connect-4", "APSFailure", "Allstate_Claims_Severity" ], "observados_en_los_resultados": [ "APSFailure", "Allstate_Claims_Severity", "Amazon_employee_access", "Internet-Advertisements", "KDDCup09_appetency", "Moneyball", "PhishingWebsites", "Satellite", "adult", "balance-scale", "banknote-authentication", "breast-w", "climate-model-simulation-crashes", "connect-4", "diabetes", "diamonds", "elevators", "house_16H", "house_prices_nominal", "house_sales", "jm1", "kc2", "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": { "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "breast-w": "auroc", "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", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "elevators": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "adult": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "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", "Satellite": "auroc", "adult": "auroc", "balance-scale": "accuracy", "banknote-authentication": "auroc", "breast-w": "auroc", "climate-model-simulation-crashes": "auroc", "connect-4": "accuracy", "diabetes": "auroc", "diamonds": "r2", "elevators": "r2", "house_16H": "r2", "house_prices_nominal": "r2", "house_sales": "r2", "jm1": "auroc", "kc2": "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", "balance-scale", "banknote-authentication", "diabetes", "house_prices_nominal", "kc2", "letter", "mfeat-factors", "micro-mass", "okcupid-stem", "ozone-level-8hr", "pc3", "pol", "sick", "wilt", "wine_quality" ], "datasets_en_los_que_el_mejor_por_media_PIERDE_la_mayoria_de_pliegues": [ "PhishingWebsites", "banknote-authentication", "diabetes", "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, 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"ganador_por_semilla": { "0": "xgboost" }, "el_ganador_cambia_con_la_semilla": false, "se_discute": false }, { "dataset": "elevators", "mejor_por_media": "lightgbm", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.031225532099654618, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 9, "sd_de_la_diferencia": 0.14265399229582984, "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, 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"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": 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"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": "lightgbm", "segundo": "sklearn.hgb", "ventaja_en_puntos": 0.01113573730195938, "pliegues_emparejados": 15, "pliegues_que_le_gana_al_segundo": 11, "sd_de_la_diferencia": 0.02173817957902454, "ganador_por_semilla": { "0": "lightgbm", "1": "xgboost", "2": "lightgbm" }, "el_ganador_cambia_con_la_semilla": true, "se_discute": true }, { "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": "lightgbm", "ventaja_en_puntos": 2.4888143176733823, "pliegues_emparejados": 12, "pliegues_que_le_gana_al_segundo": 10, "sd_de_la_diferencia": 2.6633478828779427, "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 17 de 40 datasets la ordenacion primero/segundo se discute al mirar los pliegues emparejados o al separar las semillas; en 10 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": 0, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 15, "fraccion": 0.375, "cumple_la_regla": false, "aciertos_por_ser_el_mejor": 3, "datasets_que_puede_perder_sin_incumplir": null, "datasets_que_le_faltan_para_cumplir": 17, "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 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"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", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "elevators": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "adult": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "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. 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"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 }, "elevators": { "n_medidas": 15, "media": 20.365028403057455, "sd": 0.13744012486846766, "rango": 0.40303012888246315, "n_semillas": 3, "sd_entre_semillas": 0.0038508785245789435, "rango_entre_semillas": 0.006916137210364326 }, "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 }, "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 } }, "como_hay_que_leer_este_numero": "sklearn.lineal: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.347 puntos (maxima 4.193, en kc2); rango mediano 1.091 puntos y maximo 12.679. En 13 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 0.079, 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. 15/40 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). 3 de los 15 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 17 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 0.347 puntos (maxima 4.193, en kc2); rango mediano 1.091 puntos y maximo 12.679. En 13 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 0.079, 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": 34, "fraccion": 0.85, "cumple_la_regla": true, "aciertos_por_ser_el_mejor": 7, "datasets_que_puede_perder_sin_incumplir": 2, "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 }, "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 }, "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 }, "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 }, "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 }, "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": "Satellite", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.4142620232172498, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.6079380873410721, "alto": 2.3585627418463253, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "adult", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.02721025446661418, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": -0.03489470119825144, "alto": 0.07567380562996551, "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 }, "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": "catboost", "distancia_en_puntos": 2.804232804232809, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.9034391534391508, "alto": 3.650793650793652, "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 }, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "breast-w", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.5736714975845381, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.433272946859906, "alto": 0.714107789855072, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "climate-model-simulation-crashes", 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"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 }, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "accuracy", "sin_medida": null } ], "metrica_por_dataset": { "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "breast-w": "auroc", "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", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "elevators": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "adult": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "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": 0.40692858054778597, "sd_maxima": 3.3611094680904894, "rango_mediano": 1.1179156200653324, "rango_maximo": 9.935275080906152, "dataset_mas_disperso": "pc1", "sd_entre_semillas_mediana": 0.14409988487459713, "sd_entre_semillas_maxima": 1.1261268070789852, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 16, "cuales_con_rango_mayor_que_el_liston": [ "Satellite", "balance-scale", "climate-model-simulation-crashes", "diabetes", "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 }, "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 }, "adult": { "n_medidas": 5, "media": 92.85239637556693, "sd": 0.04848546397406424, "rango": 0.11176853582701085, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "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 }, "breast-w": { "n_medidas": 15, "media": 98.76660628019323, "sd": 0.25068699927320903, "rango": 0.7472826086956559, "n_semillas": 3, "sd_entre_semillas": 0.1158511777955877, "rango_entre_semillas": 0.21286231884057827 }, "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 }, "elevators": { "n_medidas": 15, "media": 85.67983500540363, "sd": 0.2536617536513257, "rango": 0.9692897873842838, "n_semillas": 3, "sd_entre_semillas": 0.017026709622562024, "rango_entre_semillas": 0.033819176473087964 }, "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 }, "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 } }, "como_hay_que_leer_este_numero": "lightgbm: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.407 puntos (maxima 3.361, en pc1); rango mediano 1.118 puntos y maximo 9.935. 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 0.144, 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. 34/40 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). 7 de los 34 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 2 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 0.407 puntos (maxima 3.361, en pc1); rango mediano 1.118 puntos y maximo 9.935. 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 0.144, 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": 1, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 15, "fraccion": 0.375, "cumple_la_regla": false, "aciertos_por_ser_el_mejor": 2, "datasets_que_puede_perder_sin_incumplir": null, "datasets_que_le_faltan_para_cumplir": 17, "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": 0.3330567991472577, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.25774204355108576, "alto": 0.4191819704583577, "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 }, "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, 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"media": 90.82042455536431, "sd": 0.38364166014157636, "rango": 1.549053356282272, "n_semillas": 3, "sd_entre_semillas": 0.3307753368995875, "rango_entre_semillas": 0.6540447504302875 }, "kr-vs-kp": { "n_medidas": 15, "media": 99.90209744445535, "sd": 0.04829540475158423, "rango": 0.18160400510456043, "n_semillas": 3, "sd_entre_semillas": 0.030661440700282143, "rango_entre_semillas": 0.053205065279271935 }, "letter": { "n_medidas": 15, "media": 81.56754955855405, "sd": 1.2329058942166466, "rango": 4.897551224387797, "n_semillas": 3, "sd_entre_semillas": 1.0291738772743655, "rango_entre_semillas": 1.904047976011995 }, "mfeat-factors": { "n_medidas": 15, "media": 96.2, "sd": 0.9363683646331257, "rango": 4.0, "n_semillas": 3, "sd_entre_semillas": 0.614410286372228, "rango_entre_semillas": 1.1500000000000057 }, "micro-mass": { "n_medidas": 15, "media": 53.392330383480825, "sd": 5.533290707164642, "rango": 20.35398230088495, "n_semillas": 3, "sd_entre_semillas": 5.179322773731825, "rango_entre_semillas": 10.088495575221245 }, "optdigits": { "n_medidas": 15, "media": 97.77580071174378, "sd": 0.3668243439161632, "rango": 1.1565836298932481, "n_semillas": 3, "sd_entre_semillas": 0.142348754448399, "rango_entre_semillas": 0.284697508896798 }, "ozone-level-8hr": { "n_medidas": 15, "media": 91.10833333333333, "sd": 0.8204860662495372, "rango": 3.5657894736842053, "n_semillas": 3, "sd_entre_semillas": 0.5509597728359843, "rango_entre_semillas": 1.0500000000000114 }, "pc1": { "n_medidas": 15, "media": 69.72815533980582, "sd": 2.3444618089779885, "rango": 7.281553398058264, "n_semillas": 3, "sd_entre_semillas": 1.8687015646848752, "rango_entre_semillas": 3.676375404530745 }, "pc3": { "n_medidas": 15, "media": 75.73620996441281, "sd": 0.6343175273195844, "rango": 1.7126334519572879, "n_semillas": 3, "sd_entre_semillas": 0.5171299219253357, "rango_entre_semillas": 1.020907473309606 }, "pc4": { "n_medidas": 15, "media": 90.43619791666667, "sd": 0.7198214095621058, "rango": 2.701822916666657, "n_semillas": 3, "sd_entre_semillas": 0.4554862530518636, "rango_entre_semillas": 0.8897569444444287 }, "pendigits": { "n_medidas": 15, "media": 98.30529028346218, "sd": 0.20078859575729022, "rango": 0.6366530241018609, "n_semillas": 3, "sd_entre_semillas": 0.08450726005618478, "rango_entre_semillas": 0.15461573442473764 }, "pol": { "n_medidas": 15, "media": 97.73310206976478, "sd": 0.25400396398787867, "rango": 0.7760136082015094, "n_semillas": 3, "sd_entre_semillas": 0.2167690670200572, "rango_entre_semillas": 0.4112538713855258 }, "sick": { "n_medidas": 15, "media": 93.17858020142471, "sd": 1.3649481805439085, "rango": 4.182019159911576, "n_semillas": 3, "sd_entre_semillas": 1.2474285370938953, "rango_entre_semillas": 2.4213952345861003 }, "splice": { "n_medidas": 15, "media": 93.5005224660397, "sd": 0.7006278776117705, "rango": 2.6645768025078382, "n_semillas": 3, "sd_entre_semillas": 0.42791212600717754, "rango_entre_semillas": 0.8463949843260252 }, "us_crime": { "n_medidas": 15, "media": 66.65657055775947, "sd": 1.7918749269993315, "rango": 6.179062688330816, "n_semillas": 3, "sd_entre_semillas": 1.8032234632024613, "rango_entre_semillas": 3.605488106533457 }, "wilt": { "n_medidas": 15, "media": 72.82247228753779, "sd": 1.086618615741229, "rango": 3.058867987907277, "n_semillas": 3, "sd_entre_semillas": 1.2381033704073183, "rango_entre_semillas": 2.458011420893513 }, "wine_quality": { "n_medidas": 15, "media": 32.02556331409, "sd": 1.5385256990437215, "rango": 4.197850648284675, "n_semillas": 3, "sd_entre_semillas": 1.803255330601447, "rango_entre_semillas": 3.6019428393710697 }, "yeast": { "n_medidas": 12, "media": 56.459731543624166, "sd": 1.2319263494360142, "rango": 4.697986577181204, "n_semillas": 3, "sd_entre_semillas": 0.9890793726972812, "rango_entre_semillas": 1.9295302013422813 } }, "como_hay_que_leer_este_numero": "matrixai.dense.torch_cpu: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.634 puntos (maxima 21122.797, en house_prices_nominal); rango mediano 2.116 puntos y maximo 78425.037. En 19 de 37 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 0.583, maxima 14043.341 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. 15/40 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 15 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 17 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 0.634 puntos (maxima 21122.797, en house_prices_nominal); rango mediano 2.116 puntos y maximo 78425.037. En 19 de 37 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 0.583, maxima 14043.341 puntos." }, "sklearn.hgb": { "motor": "sklearn.hgb", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 3, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 34, "fraccion": 0.85, "cumple_la_regla": true, "aciertos_por_ser_el_mejor": 10, "datasets_que_puede_perder_sin_incumplir": 2, "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 }, "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 }, "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 }, "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 }, "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 }, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Satellite", "cumple": true, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 1.2267551133222754, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.33608623548921923, "alto": 2.147385295743504, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "adult", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.0, "segundo": "lightgbm", "ventaja_sobre_el_segundo_en_puntos": 0.02721025446661418, "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": false, "perdido_por_fallo": false, "mejor": "catboost", "distancia_en_puntos": 3.756613756613758, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.961640211640211, "alto": 4.55026455026455, "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 }, "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": "breast-w", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.lineal", "distancia_en_puntos": 0.5948067632850162, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.4498414855072453, "alto": 0.7216183574879217, "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 }, "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 }, "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": "matrixai.dense.torch_cpu", "distancia_en_puntos": 4.281481481481486, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 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"micro-mass": "accuracy", "balance-scale": "accuracy", "breast-w": "auroc", "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", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "elevators": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "adult": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "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": 0.5238825210657251, "sd_maxima": 4.0997319060835755, "rango_mediano": 1.4709509036123904, "rango_maximo": 11.521035598705495, "dataset_mas_disperso": "pc1", "sd_entre_semillas_mediana": 0.07645603876285907, "sd_entre_semillas_maxima": 2.0615775790779374, "liston_en_puntos": 2.0, "datasets_con_rango_mayor_que_el_liston": 17, "cuales_con_rango_mayor_que_el_liston": [ "Amazon_employee_access", "Satellite", "balance-scale", "climate-model-simulation-crashes", "diabetes", "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 }, "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 }, "adult": { "n_medidas": 5, "media": 92.87960663003354, "sd": 0.06701428478401446, "rango": 0.15568418438364517, "n_semillas": 1, "sd_entre_semillas": null, "rango_entre_semillas": 0.0 }, "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 }, "breast-w": { "n_medidas": 15, "media": 98.74547101449275, "sd": 0.25473566463430003, "rango": 0.8605072463768124, "n_semillas": 3, "sd_entre_semillas": 0.056567010855059295, "rango_entre_semillas": 0.10869565217392108 }, "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 }, "elevators": { "n_medidas": 15, "media": 85.64860947330399, "sd": 0.23858878505611059, "rango": 0.8002955538480307, "n_semillas": 3, "sd_entre_semillas": 0.03235879578523407, "rango_entre_semillas": 0.060173141559815235 }, "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 }, "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 } }, "como_hay_que_leer_este_numero": "sklearn.hgb: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.524 puntos (maxima 4.100, en pc1); rango mediano 1.471 puntos y maximo 11.521. En 17 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 0.076, 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. 34/40 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). 10 de los 34 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 2 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 0.524 puntos (maxima 4.100, en pc1); rango mediano 1.471 puntos y maximo 11.521. En 17 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 0.076, maxima 2.062 puntos." }, "xgboost": { "motor": "xgboost", "metrica": "auroc", "puntos_exigidos": 2.0, "intervalos_de_los_que_NO_cumplen_que_cruzan_el_liston": 6, "n_medidas_sin_pliegue_declarado": 0, "fraccion_minima": 0.8, "datasets": 40, "cumplidos": 30, "fraccion": 0.75, "cumple_la_regla": false, "aciertos_por_ser_el_mejor": 5, "datasets_que_puede_perder_sin_incumplir": null, "datasets_que_le_faltan_para_cumplir": 2, "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 }, "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 }, "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 }, "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 }, "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 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"accuracy", "sin_medida": null } ], "metrica_por_dataset": { "kc2": "auroc", "climate-model-simulation-crashes": "auroc", "micro-mass": "accuracy", "balance-scale": "accuracy", "breast-w": "auroc", "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", "wilt": "auroc", "Satellite": "auroc", "optdigits": "accuracy", "wine_quality": "r2", "jm1": "auroc", "pendigits": "accuracy", "PhishingWebsites": "auroc", "pol": "r2", "elevators": "r2", "letter": "accuracy", "house_sales": "r2", "house_16H": "r2", "Amazon_employee_access": "auroc", "adult": "auroc", "KDDCup09_appetency": "auroc", "okcupid-stem": "accuracy", "diamonds": "r2", "connect-4": "accuracy", "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. 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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 } }, "como_hay_que_leer_este_numero": "xgboost: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.492 puntos (maxima 3.005, en pc1); rango mediano 1.413 puntos y maximo 9.612. En 17 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. 30/40 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). 5 de los 30 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 2 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 0.492 puntos (maxima 3.005, en pc1); rango mediano 1.413 puntos y maximo 9.612. En 17 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": 33, "fraccion": 0.825, "cumple_la_regla": true, "aciertos_por_ser_el_mejor": 13, "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": 0.0, "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 }, "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 }, "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 }, "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 }, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "Satellite", "cumple": false, "perdido_por_fallo": false, "mejor": "matrixai.dense.torch_cpu", "distancia_en_puntos": 3.858706467661699, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 2.760331674958541, "alto": 5.101442786069653, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "auroc", "sin_medida": null }, { "dataset": "adult", "cumple": true, "perdido_por_fallo": false, "mejor": "sklearn.hgb", "distancia_en_puntos": 0.5231395131026328, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 0.4892703645180263, "alto": 0.5521008655468274, "nivel": 0.95, "unidad": "puntos porcentuales de la metrica, igual que distancia_en_puntos", "emparejado_por": "repeticion y pliegue", 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"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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "sin_medida": null }, { "dataset": "elevators", "cumple": true, "perdido_por_fallo": false, "mejor": "lightgbm", "distancia_en_puntos": 1.1589220657039156, "segundo": null, "ventaja_sobre_el_segundo_en_puntos": null, "intervalo_de_la_distancia": { "bajo": 1.026811906470194, "alto": 1.2906151984060805, "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 }, "motores_que_compitieron": [ "catboost", "lightgbm", "matrixai.dense.torch_cpu", "sklearn.hgb", "sklearn.lineal", "xgboost" ], "metrica": "r2", "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 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"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 }, "elevators": { "n_medidas": 15, "media": 84.52091293969973, "sd": 0.21920911892276282, "rango": 0.7980440130578046, "n_semillas": 3, "sd_entre_semillas": 0.10708962260551506, "rango_entre_semillas": 0.19842295493950246 }, "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 }, "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 } }, "como_hay_que_leer_este_numero": "catboost: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.390 puntos (maxima 2.728, en kc2); rango mediano 1.321 puntos y maximo 8.204. 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 0.167, 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. 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). 13 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. catboost: la media de cada dataset sale de 5 medidas cuya desviacion tipica mediana es 0.390 puntos (maxima 2.728, en kc2); rango mediano 1.321 puntos y maximo 8.204. 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 0.167, maxima 1.160 puntos." } } }, "digest_resultados_crudos": "3646af61b155c1321ad3fd71f25e9300ed9797b9b5dec0f37ed835cd835ecf48" }