10576234 28997 Marc Boel 16 Supervised Classification 19037 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),gradientboostingclassifier=sklearn.ensemble._gb.GradientBoostingClassifier)(1) 8292516 Python_3.8.10. Sklearn_0.24.2. NumPy_1.17.4. SciPy_1.3.3. add_indicator false 18819 copy true 18819 fill_value null 18819 missing_values NaN 18819 strategy "most_frequent" 18819 verbose 0 18819 n_jobs null 18996 remainder "drop" 18996 sparse_threshold 0.3 18996 transformer_weights null 18996 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}] 18996 verbose false 18996 categories "auto" 18997 drop null 18997 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 18997 handle_unknown "ignore" 18997 sparse true 18997 memory null 19037 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}] 19037 verbose false 19037 ccp_alpha 0.0 19038 criterion "friedman_mse" 19038 init null 19038 learning_rate 0.6882440565138986 19038 loss "deviance" 19038 max_depth 3 19038 max_features null 19038 max_leaf_nodes 111 19038 min_impurity_decrease 0.0 19038 min_impurity_split null 19038 min_samples_leaf 53 19038 min_samples_split 2 19038 min_weight_fraction_leaf 0.0 19038 n_estimators 100 19038 n_iter_no_change 1 19038 random_state 28827 19038 subsample 1.0 19038 tol 0.0001 19038 validation_fraction 0.3066140473238077 19038 verbose 0 19038 warm_start false 19038 openml-python Sklearn_0.24.2. 16 mfeat-karhunen https://www.openml.org/data/download/16/dataset_16_mfeat-karhunen.arff -1 22078735 description https://api.openml.org/data/download/22078735/description.xml -1 22078737 predictions https://api.openml.org/data/download/22078737/predictions.arff area_under_roc_curve 0.9942619444444443 [0.999281,0.993792,0.999619,0.992519,0.996603,0.987664,0.9917,0.997075,0.991853,0.992514] average_cost 0 f_measure 0.9199320660495018 [0.967742,0.879012,0.962406,0.886076,0.938272,0.880597,0.928934,0.945545,0.891139,0.919598] kappa 0.9111111111111112 kb_relative_information_score 0.9199521524481291 mean_absolute_error 0.01831884339499314 mean_prior_absolute_error 0.18000000000000554 weighted_recall 0.92 [0.975,0.89,0.96,0.875,0.95,0.885,0.915,0.955,0.88,0.915] number_of_instances 2000 [200,200,200,200,200,200,200,200,200,200] precision 0.920059073170834 [0.960591,0.868293,0.964824,0.897436,0.926829,0.876238,0.943299,0.936275,0.902564,0.924242] predictive_accuracy 0.92 prior_entropy 3.3219280948872383 relative_absolute_error 0.1017713521944032 root_mean_prior_squared_error 0.3000000000000046 root_mean_squared_error 0.11006382691931582 root_relative_squared_error 0.3668794230643805 total_cost 0 unweighted_recall 0.9199999999999999 [0.975,0.89,0.96,0.875,0.95,0.885,0.915,0.955,0.88,0.915] area_under_roc_curve 0.9954444444444445 [0.998611,0.989167,1,0.997222,0.999722,0.993611,0.999722,1,0.979167,0.997222] area_under_roc_curve 0.9945277777777777 [1,0.995556,1,0.989444,1,0.991667,0.994167,0.988611,0.998889,0.986944] area_under_roc_curve 0.9961111111111113 [0.999722,0.996667,0.999444,0.994444,0.997778,0.986111,0.999167,0.995278,0.996111,0.996389] area_under_roc_curve 0.9968333333333332 [1,0.9975,1,0.998611,0.991111,0.990833,0.995833,1,0.996667,0.997778] area_under_roc_curve 0.9921111111111113 [0.999444,0.990278,0.999167,0.981389,0.991667,0.967778,0.998611,0.999167,0.993889,0.999722] area_under_roc_curve 0.9932777777777778 [1,0.995833,0.998056,0.998333,0.991944,0.989444,0.996111,0.996667,0.993333,0.973056] area_under_roc_curve 0.9907777777777779 [0.994444,0.985,0.998611,0.9925,1,0.991111,1,0.996111,0.983333,0.966667] area_under_roc_curve 0.9972777777777777 [1,0.991111,0.999722,0.994722,0.999722,0.993056,0.998889,0.999167,0.996389,1] area_under_roc_curve 0.997 [1,0.998333,1,0.985556,0.999444,0.988333,0.998333,1,1,1] area_under_roc_curve 0.9908888888888887 [1,0.993889,1,0.994722,1,1,0.941667,0.999444,0.981944,0.997222] 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 0.935316222074887 [0.974359,0.878049,1,0.9,0.95,0.904762,0.947368,1,0.871795,0.926829] f_measure 0.9349301725232405 [1,0.95,1,0.829268,0.974359,0.842105,0.974359,0.926829,0.952381,0.9] f_measure 0.8939247286615708 [0.952381,0.810811,0.95,0.871795,0.857143,0.85,0.952381,0.894737,0.9,0.9] f_measure 0.9352812848255724 [1,0.95,0.97561,0.894737,0.926829,0.923077,0.878049,1,0.857143,0.947368] f_measure 0.8945815370705659 [0.95,0.780488,0.894737,0.820513,0.9,0.904762,0.947368,0.930233,0.842105,0.97561] f_measure 0.9141295781668053 [0.952381,0.904762,0.926829,0.9,0.95,0.923077,0.871795,0.97561,0.842105,0.894737] f_measure 0.9154346657909663 [0.947368,0.837209,0.947368,0.95,1,0.878049,1,0.878049,0.926829,0.789474] f_measure 0.9133906063353475 [0.97561,0.820513,0.95,0.871795,0.930233,0.842105,0.95,0.952381,0.888889,0.952381] f_measure 0.9394043815995035 [0.952381,0.9,1,0.9,0.926829,0.864865,0.9,0.97561,0.974359,1] f_measure 0.9203134581448473 [0.974359,0.952381,0.97561,0.923077,0.974359,0.869565,0.864865,0.918919,0.85,0.9] kappa 0.9277777777777778 kappa 0.9277777777777778 kappa 0.8833333333333333 kappa 0.9277777777777778 kappa 0.8833333333333333 kappa 0.9055555555555556 kappa 0.9055555555555556 kappa 0.9055555555555556 kappa 0.9333333333333332 kappa 0.9111111111111112 kb_relative_information_score 0.9306967354465148 kb_relative_information_score 0.9356297613835092 kb_relative_information_score 0.9054718431549952 kb_relative_information_score 0.9389417945578458 kb_relative_information_score 0.9014816666670956 kb_relative_information_score 0.9080443286334521 kb_relative_information_score 0.8997142542383455 kb_relative_information_score 0.9139929937566247 kb_relative_information_score 0.9435234887453021 kb_relative_information_score 0.9220246578972968 mean_absolute_error 0.015565065358952685 mean_absolute_error 0.014397214035920624 mean_absolute_error 0.022297149842543513 mean_absolute_error 0.013495466270638407 mean_absolute_error 0.023504904029745272 mean_absolute_error 0.020829673080990076 mean_absolute_error 0.023717873878110654 mean_absolute_error 0.019558645545872336 mean_absolute_error 0.012600607806475204 mean_absolute_error 0.017221834100682957 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 mean_prior_absolute_error 0.1799999999999998 number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] number_of_instances 200 [20,20,20,20,20,20,20,20,20,20] precision 0.9370277967646389 [1,0.857143,1,0.9,0.95,0.863636,1,1,0.894737,0.904762] precision 0.9362265512265513 [1,0.95,1,0.809524,1,0.888889,1,0.904762,0.909091,0.9] precision 0.8957897864089814 [0.909091,0.882353,0.95,0.894737,0.818182,0.85,0.909091,0.944444,0.9,0.9] precision 0.9374280397964608 [1,0.95,0.952381,0.944444,0.904762,0.947368,0.857143,1,0.818182,1] precision 0.897292589180461 [0.95,0.761905,0.944444,0.842105,0.9,0.863636,1,0.869565,0.888889,0.952381] precision 0.9155308726361356 [0.909091,0.863636,0.904762,0.9,0.95,0.947368,0.894737,0.952381,0.888889,0.944444] precision 0.9184989648033125 [1,0.782609,1,0.95,1,0.857143,1,0.857143,0.904762,0.833333] precision 0.9165858982106121 [0.952381,0.842105,0.95,0.894737,0.869565,0.888889,0.95,0.909091,1,0.909091] precision 0.9407410236822003 [0.909091,0.9,1,0.9,0.904762,0.941176,0.9,0.952381,1,1] precision 0.9269247522343497 [1,0.909091,0.952381,0.947368,1,0.769231,0.941176,1,0.85,0.9] predictive_accuracy 0.935 predictive_accuracy 0.935 predictive_accuracy 0.895 predictive_accuracy 0.935 predictive_accuracy 0.895 predictive_accuracy 0.915 predictive_accuracy 0.915 predictive_accuracy 0.915 predictive_accuracy 0.94 predictive_accuracy 0.92 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 prior_entropy 3.321928094887355 relative_absolute_error 0.08647258532751502 relative_absolute_error 0.07998452242178133 relative_absolute_error 0.12387305468079743 relative_absolute_error 0.0749748126146579 relative_absolute_error 0.13058280016525167 relative_absolute_error 0.11572040600550056 relative_absolute_error 0.13176596598950377 relative_absolute_error 0.1086591419215131 relative_absolute_error 0.07000337670264009 relative_absolute_error 0.09567685611490542 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_prior_squared_error 0.2999999999999998 root_mean_squared_error 0.1005110197585496 root_mean_squared_error 0.10206433113809343 root_mean_squared_error 0.11784930244375837 root_mean_squared_error 0.10035171595459663 root_mean_squared_error 0.1226972241755272 root_mean_squared_error 0.11533986001374698 root_mean_squared_error 0.11976777410464158 root_mean_squared_error 0.11334724102618077 root_mean_squared_error 0.09390921219294072 root_mean_squared_error 0.11087467326162435 root_relative_squared_error 0.33503673252849886 root_relative_squared_error 0.3402144371269783 root_relative_squared_error 0.3928310081458614 root_relative_squared_error 0.3345057198486557 root_relative_squared_error 0.4089907472517576 root_relative_squared_error 0.3844662000458235 root_relative_squared_error 0.3992259136821389 root_relative_squared_error 0.3778241367539361 root_relative_squared_error 0.31303070730980265 root_relative_squared_error 0.36958224420541474 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 0.9349999999999999 [0.95,0.9,1,0.9,0.95,0.95,0.9,1,0.85,0.95] unweighted_recall 0.9349999999999999 [1,0.95,1,0.85,0.95,0.8,0.95,0.95,1,0.9] unweighted_recall 0.8949999999999999 [1,0.75,0.95,0.85,0.9,0.85,1,0.85,0.9,0.9] unweighted_recall 0.9350000000000002 [1,0.95,1,0.85,0.95,0.9,0.9,1,0.9,0.9] unweighted_recall 0.8950000000000001 [0.95,0.8,0.85,0.8,0.9,0.95,0.9,1,0.8,1] unweighted_recall 0.915 [1,0.95,0.95,0.9,0.95,0.9,0.85,1,0.8,0.85] unweighted_recall 0.915 [0.9,0.9,0.9,0.95,1,0.9,1,0.9,0.95,0.75] unweighted_recall 0.915 [1,0.8,0.95,0.85,1,0.8,0.95,1,0.8,1] unweighted_recall 0.9400000000000001 [1,0.9,1,0.9,0.95,0.8,0.9,1,0.95,1] unweighted_recall 0.9199999999999999 [0.95,1,1,0.9,0.95,1,0.8,0.85,0.85,0.9] usercpu_time_millis 3773.367046000203 usercpu_time_millis 3758.7447460000476 usercpu_time_millis 3199.7971400005554 usercpu_time_millis 4002.027246999205 usercpu_time_millis 3302.2068410009524 usercpu_time_millis 2541.6196299993317 usercpu_time_millis 2379.1372300001967 usercpu_time_millis 2854.7340360000817 usercpu_time_millis 3626.7557460005264 usercpu_time_millis 3145.317636999607 usercpu_time_millis_testing 2.659200000380224 usercpu_time_millis_testing 2.4819999998726416 usercpu_time_millis_testing 2.6324999998905696 usercpu_time_millis_testing 2.4116999993566424 usercpu_time_millis_testing 3.045100000235834 usercpu_time_millis_testing 2.4808999996821512 usercpu_time_millis_testing 3.041299999495095 usercpu_time_millis_testing 3.2363999998779036 usercpu_time_millis_testing 2.4934000002758694 usercpu_time_millis_testing 2.4868000000424217 usercpu_time_millis_training 3770.707845999823 usercpu_time_millis_training 3756.262746000175 usercpu_time_millis_training 3197.164640000665 usercpu_time_millis_training 3999.6155469998484 usercpu_time_millis_training 3299.1617410007166 usercpu_time_millis_training 2539.1387299996495 usercpu_time_millis_training 2376.0959300007016 usercpu_time_millis_training 2851.497636000204 usercpu_time_millis_training 3624.2623460002505 usercpu_time_millis_training 3142.830836999565 wall_clock_time_millis 3782.2272777557373 wall_clock_time_millis 3761.240243911743 wall_clock_time_millis 3202.012538909912 wall_clock_time_millis 4003.981351852417 wall_clock_time_millis 3302.950620651245 wall_clock_time_millis 2562.913179397583 wall_clock_time_millis 2379.375219345093 wall_clock_time_millis 2856.6198348999023 wall_clock_time_millis 3627.286195755005 wall_clock_time_millis 3157.5703620910645 wall_clock_time_millis_testing 2.663135528564453 wall_clock_time_millis_testing 2.485513687133789 wall_clock_time_millis_testing 2.6357173919677734 wall_clock_time_millis_testing 2.415180206298828 wall_clock_time_millis_testing 3.072023391723633 wall_clock_time_millis_testing 2.483844757080078 wall_clock_time_millis_testing 3.0455589294433594 wall_clock_time_millis_testing 3.2401084899902344 wall_clock_time_millis_testing 2.496480941772461 wall_clock_time_millis_testing 2.4900436401367188 wall_clock_time_millis_training 3779.564142227173 wall_clock_time_millis_training 3758.7547302246094 wall_clock_time_millis_training 3199.3768215179443 wall_clock_time_millis_training 4001.566171646118 wall_clock_time_millis_training 3299.8785972595215 wall_clock_time_millis_training 2560.429334640503 wall_clock_time_millis_training 2376.3296604156494 wall_clock_time_millis_training 2853.379726409912 wall_clock_time_millis_training 3624.7897148132324 wall_clock_time_millis_training 3155.0803184509277 weighted_recall 0.935 [0.95,0.9,1,0.9,0.95,0.95,0.9,1,0.85,0.95] weighted_recall 0.935 [1,0.95,1,0.85,0.95,0.8,0.95,0.95,1,0.9] weighted_recall 0.895 [1,0.75,0.95,0.85,0.9,0.85,1,0.85,0.9,0.9] weighted_recall 0.935 [1,0.95,1,0.85,0.95,0.9,0.9,1,0.9,0.9] weighted_recall 0.895 [0.95,0.8,0.85,0.8,0.9,0.95,0.9,1,0.8,1] weighted_recall 0.915 [1,0.95,0.95,0.9,0.95,0.9,0.85,1,0.8,0.85] weighted_recall 0.915 [0.9,0.9,0.9,0.95,1,0.9,1,0.9,0.95,0.75] weighted_recall 0.915 [1,0.8,0.95,0.85,1,0.8,0.95,1,0.8,1] weighted_recall 0.94 [1,0.9,1,0.9,0.95,0.8,0.9,1,0.95,1] weighted_recall 0.92 [0.95,1,1,0.9,0.95,1,0.8,0.85,0.85,0.9]