10554596 8323 Heinrich Peters 9977 Supervised Classification 18607 sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2) 8275822 copy true 17405 with_mean true 17405 with_std true 17405 add_indicator false 17407 copy true 17407 fill_value null 17407 missing_values NaN 17407 strategy "most_frequent" 17407 verbose 0 17407 categorical_features null 17408 categories null 17408 drop null 17408 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 17408 handle_unknown "ignore" 17408 n_values null 17408 sparse true 17408 C 0.01 17462 class_weight null 17462 dual false 17462 fit_intercept true 17462 intercept_scaling 1 17462 l1_ratio null 17462 max_iter 100 17462 multi_class "warn" 17462 n_jobs null 17462 penalty "l2" 17462 random_state 1 17462 solver "liblinear" 17462 tol 0.0001 17462 verbose 0 17462 warm_start false 17462 n_jobs null 18299 remainder "drop" 18299 sparse_threshold 0.3 18299 transformer_weights null 18299 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, true, true, true, false, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true, true, false, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, false, false, false, true, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false, false, true, false, false]}}] 18299 verbose false 18299 memory null 18300 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 18300 verbose false 18300 memory null 18301 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] 18301 verbose false 18301 memory null 18607 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}] 18607 verbose false 18607 openml-python Sklearn_0.21.2. 1486 nomao https://www.openml.org/data/download/1592278/phpDYCOet -1 22034147 description https://api.openml.org/data/download/22034147/description.xml -1 22034148 predictions https://api.openml.org/data/download/22034148/predictions.arff area_under_roc_curve 0.9861427481167114 [0.986143,0.986143] average_cost 0 f_measure 0.9449556306737025 [0.903044,0.961713] kappa 0.8647610200655561 kb_relative_information_score 0.7881553607347425 mean_absolute_error 0.08989349473976488 mean_prior_absolute_error 0.4080904194746198 weighted_recall 0.9451037284201363 [0.895063,0.965111] number_of_instances 34465 [9844,24621] precision 0.9448655869053608 [0.911169,0.958338] predictive_accuracy 0.9451037284201363 prior_entropy 0.8629999933345911 relative_absolute_error 0.22027837569795136 root_mean_prior_squared_error 0.4517106852187937 root_mean_squared_error 0.2010416510540189 root_relative_squared_error 0.4450672911503986 total_cost 0 unweighted_recall 0.9300870332806913 [0.895063,0.965111] area_under_roc_curve 0.9848798807720112 [0.98488,0.98488] area_under_roc_curve 0.9864436902852289 [0.986444,0.986444] area_under_roc_curve 0.9843950896262789 [0.984395,0.984395] area_under_roc_curve 0.9866379114829676 [0.986638,0.986638] area_under_roc_curve 0.9859562816743433 [0.985956,0.985956] area_under_roc_curve 0.9870036753779398 [0.987004,0.987004] area_under_roc_curve 0.9850681579520914 [0.985068,0.985068] area_under_roc_curve 0.9893684822307199 [0.989368,0.989368] area_under_roc_curve 0.9861058825860395 [0.986106,0.986106] area_under_roc_curve 0.9860608897518707 [0.986061,0.986061] average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 f_measure 0.9437904376714694 [0.900669,0.961018] f_measure 0.9465381704479006 [0.906122,0.962708] f_measure 0.940763715976133 [0.89613,0.958621] f_measure 0.9479828665772478 [0.908627,0.963728] f_measure 0.9422066826746136 [0.898625,0.959643] f_measure 0.942478984753484 [0.897875,0.960306] f_measure 0.9469830892490567 [0.907529,0.962752] f_measure 0.9481852218985067 [0.908624,0.963997] f_measure 0.9464723962123756 [0.905738,0.962753] f_measure 0.9441202282063188 [0.900365,0.961608] kappa 0.86169603025565 kappa 0.8688315475341081 kappa 0.8547515759268524 kappa 0.872356918119619 kappa 0.8582683461572076 kappa 0.8582033405645533 kappa 0.8702825521642202 kappa 0.8726262839372246 kappa 0.8684942325815999 kappa 0.8620108272020978 kb_relative_information_score 0.7838023509698457 kb_relative_information_score 0.7892680255506116 kb_relative_information_score 0.7769044411648407 kb_relative_information_score 0.7886538247981687 kb_relative_information_score 0.7863177757657751 kb_relative_information_score 0.7871564569625893 kb_relative_information_score 0.7837715367392984 kb_relative_information_score 0.8048989994390956 kb_relative_information_score 0.7934494847929043 kb_relative_information_score 0.7873383991362018 mean_absolute_error 0.09167015939826498 mean_absolute_error 0.08958895669717684 mean_absolute_error 0.09397873670812144 mean_absolute_error 0.09022796136932464 mean_absolute_error 0.09014076127875591 mean_absolute_error 0.09006845099912786 mean_absolute_error 0.09155023292005728 mean_absolute_error 0.08316395369563401 mean_absolute_error 0.08823799661552925 mean_absolute_error 0.09030595620034827 mean_prior_absolute_error 0.4080229059806574 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.4081472833897437 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 mean_prior_absolute_error 0.40805842175852763 number_of_instances 3447 [984,2463] number_of_instances 3447 [985,2462] number_of_instances 3447 [985,2462] number_of_instances 3447 [985,2462] number_of_instances 3447 [985,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] number_of_instances 3446 [984,2462] precision 0.9436917540507191 [0.912409,0.95619] precision 0.9464752210994194 [0.910769,0.960761] precision 0.9407161374673573 [0.898876,0.957455] precision 0.9479181602524187 [0.913758,0.961585] precision 0.9421540192911927 [0.90184,0.958283] precision 0.9424201237707758 [0.916402,0.952819] precision 0.947094137230634 [0.902513,0.964912] precision 0.9481017816615376 [0.91805,0.960113] precision 0.9463892200375937 [0.913223,0.959645] precision 0.9441756425306933 [0.924973,0.95185] predictive_accuracy 0.944009283434871 predictive_accuracy 0.946620249492312 predictive_accuracy 0.9408181026979983 predictive_accuracy 0.9480707861908907 predictive_accuracy 0.9422686393965768 predictive_accuracy 0.9428322692977364 predictive_accuracy 0.9468949506674404 predictive_accuracy 0.9483459082994777 predictive_accuracy 0.946604759141033 predictive_accuracy 0.944573418456181 prior_entropy 0.862791736866817 prior_entropy 0.863175399426382 prior_entropy 0.863175399426382 prior_entropy 0.863175399426382 prior_entropy 0.863175399426382 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 prior_entropy 0.862901291120622 relative_absolute_error 0.2246691498310408 relative_absolute_error 0.21950153864341052 relative_absolute_error 0.23025692080468965 relative_absolute_error 0.22106716139321964 relative_absolute_error 0.22085351280576732 relative_absolute_error 0.22072440169468358 relative_absolute_error 0.22435570016058382 relative_absolute_error 0.20380403702302963 relative_absolute_error 0.2162386362111279 relative_absolute_error 0.22130643894365612 root_mean_prior_squared_error 0.4516359481339797 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.45177362368332785 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_prior_squared_error 0.4516752654560011 root_mean_squared_error 0.204929474762538 root_mean_squared_error 0.20050984010633258 root_mean_squared_error 0.20891995894816948 root_mean_squared_error 0.19865056394909011 root_mean_squared_error 0.20393029934261567 root_mean_squared_error 0.1994462786268684 root_mean_squared_error 0.2030735441824375 root_mean_squared_error 0.1914148241186558 root_mean_squared_error 0.19678898190249783 root_mean_squared_error 0.20222608644667475 root_relative_squared_error 0.45374925448083414 root_relative_squared_error 0.4438281245185765 root_relative_squared_error 0.4624439055224981 root_relative_squared_error 0.43971262051441684 root_relative_squared_error 0.45139930410271417 root_relative_squared_error 0.4415700700934153 root_relative_squared_error 0.4496007634542907 root_relative_squared_error 0.4237885905162589 root_relative_squared_error 0.43568686831638687 root_relative_squared_error 0.4477245089843737 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 unweighted_recall 0.927561445985958 [0.889228,0.965895] unweighted_recall 0.933092859175199 [0.901523,0.964663] unweighted_recall 0.926594902415188 [0.893401,0.959789] unweighted_recall 0.9347173483652018 [0.903553,0.965881] unweighted_recall 0.9282193916051908 [0.895431,0.961007] unweighted_recall 0.9239967836315244 [0.880081,0.967912] unweighted_recall 0.9366013816515095 [0.912602,0.960601] unweighted_recall 0.9336512551762398 [0.89939,0.967912] unweighted_recall 0.9321276904889276 [0.898374,0.965881] unweighted_recall 0.9243001756784424 [0.877033,0.971568] usercpu_time_millis 2015.2100000123028 usercpu_time_millis 1918.47200000484 usercpu_time_millis 1928.0020000005607 usercpu_time_millis 1939.8140000048443 usercpu_time_millis 1927.1460000018124 usercpu_time_millis 2026.6699999992852 usercpu_time_millis 1961.9140000068 usercpu_time_millis 1972.7859999984503 usercpu_time_millis 1989.0979999981937 usercpu_time_millis 2012.697999998636 usercpu_time_millis_testing 32.10800000670133 usercpu_time_millis_testing 33.05000000545988 usercpu_time_millis_testing 31.96400000160793 usercpu_time_millis_testing 32.26600000198232 usercpu_time_millis_testing 36.09600000345381 usercpu_time_millis_testing 32.5860000011744 usercpu_time_millis_testing 41.154000005917624 usercpu_time_millis_testing 32.569999995757826 usercpu_time_millis_testing 37.210000002232846 usercpu_time_millis_testing 43.897999996261206 usercpu_time_millis_training 1983.1020000056014 usercpu_time_millis_training 1885.4219999993802 usercpu_time_millis_training 1896.0379999989527 usercpu_time_millis_training 1907.548000002862 usercpu_time_millis_training 1891.0499999983585 usercpu_time_millis_training 1994.0839999981108 usercpu_time_millis_training 1920.7600000008824 usercpu_time_millis_training 1940.2160000026925 usercpu_time_millis_training 1951.8879999959609 usercpu_time_millis_training 1968.8000000023749 wall_clock_time_millis 1080.7409286499023 wall_clock_time_millis 1035.2871417999268 wall_clock_time_millis 1036.7670059204102 wall_clock_time_millis 1038.485050201416 wall_clock_time_millis 1033.61177444458 wall_clock_time_millis 1089.749813079834 wall_clock_time_millis 1051.3241291046143 wall_clock_time_millis 1052.0920753479004 wall_clock_time_millis 1064.093828201294 wall_clock_time_millis 1076.9221782684326 wall_clock_time_millis_testing 22.940874099731445 wall_clock_time_millis_testing 24.026870727539062 wall_clock_time_millis_testing 22.176027297973633 wall_clock_time_millis_testing 22.64690399169922 wall_clock_time_millis_testing 25.3298282623291 wall_clock_time_millis_testing 22.97687530517578 wall_clock_time_millis_testing 28.15413475036621 wall_clock_time_millis_testing 23.380041122436523 wall_clock_time_millis_testing 25.54798126220703 wall_clock_time_millis_testing 29.9680233001709 wall_clock_time_millis_training 1057.800054550171 wall_clock_time_millis_training 1011.2602710723877 wall_clock_time_millis_training 1014.5909786224365 wall_clock_time_millis_training 1015.8381462097168 wall_clock_time_millis_training 1008.281946182251 wall_clock_time_millis_training 1066.7729377746582 wall_clock_time_millis_training 1023.169994354248 wall_clock_time_millis_training 1028.7120342254639 wall_clock_time_millis_training 1038.545846939087 wall_clock_time_millis_training 1046.9541549682617 weighted_recall 0.9440092834348709 [0.889228,0.965895] weighted_recall 0.9466202494923122 [0.901523,0.964663] weighted_recall 0.9408181026979983 [0.893401,0.959789] weighted_recall 0.9480707861908906 [0.903553,0.965881] weighted_recall 0.9422686393965767 [0.895431,0.961007] weighted_recall 0.9428322692977366 [0.880081,0.967912] weighted_recall 0.9468949506674406 [0.912602,0.960601] weighted_recall 0.9483459082994776 [0.89939,0.967912] weighted_recall 0.946604759141033 [0.898374,0.965881] weighted_recall 0.944573418456181 [0.877033,0.971568]