10576155 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) 8292437 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 "mean" 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 0.5823557371711975 19038 loss "deviance" 19038 max_depth 3 19038 max_features null 19038 max_leaf_nodes 1817 19038 min_impurity_decrease 0.0 19038 min_impurity_split null 19038 min_samples_leaf 159 19038 min_samples_split 2 19038 min_weight_fraction_leaf 0.0 19038 n_estimators 100 19038 n_iter_no_change 7 19038 random_state 32559 19038 subsample 1.0 19038 tol 0.0001 19038 validation_fraction 0.12321250256377692 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 22078577 description https://api.openml.org/data/download/22078577/description.xml -1 22078578 predictions https://api.openml.org/data/download/22078578/predictions.arff area_under_roc_curve 0.9973408333333333 [0.999433,0.997831,0.999903,0.996267,0.998036,0.995464,0.995044,0.998794,0.995586,0.99705] average_cost 0 f_measure 0.9514902759902966 [0.97,0.935961,0.982544,0.945,0.95,0.927681,0.964646,0.967742,0.933673,0.937656] kappa 0.9461111111111111 kb_relative_information_score 0.9472847983172695 mean_absolute_error 0.01272488088271317 mean_prior_absolute_error 0.18000000000000554 weighted_recall 0.9515 [0.97,0.95,0.985,0.945,0.95,0.93,0.955,0.975,0.915,0.94] number_of_instances 2000 [200,200,200,200,200,200,200,200,200,200] precision 0.9516332045911171 [0.97,0.92233,0.9801,0.945,0.95,0.925373,0.97449,0.960591,0.953125,0.935323] predictive_accuracy 0.9515 prior_entropy 3.3219280948872383 relative_absolute_error 0.07069378268173765 root_mean_prior_squared_error 0.3000000000000046 root_mean_squared_error 0.088871302846153 root_relative_squared_error 0.2962376761538388 total_cost 0 unweighted_recall 0.9514999999999999 [0.97,0.95,0.985,0.945,0.95,0.93,0.955,0.975,0.915,0.94] area_under_roc_curve 0.9973333333333333 [0.998333,0.995833,1,0.999444,0.999722,0.994167,0.999722,1,0.986389,0.999722] area_under_roc_curve 0.996861111111111 [1,0.9975,1,0.982778,0.998333,0.997222,0.998889,0.997778,1,0.996111] area_under_roc_curve 0.997638888888889 [0.998889,0.999444,1,1,0.999722,0.989167,1,0.995278,0.998889,0.995] area_under_roc_curve 0.9984722222222223 [0.999722,0.998611,1,0.997222,0.996389,0.997778,1,0.998889,0.999444,0.996667] area_under_roc_curve 0.99625 [0.998611,0.995,1,0.991111,0.994722,0.988056,1,0.998611,0.997778,0.998611] area_under_roc_curve 0.9980833333333335 [1,0.998611,1,1,0.996944,0.994722,0.9975,0.999722,0.998333,0.995] area_under_roc_curve 0.9981111111111112 [1,0.996389,0.999444,0.999444,1,0.996111,1,0.999167,0.994722,0.995833] area_under_roc_curve 0.9990277777777777 [1,0.999444,1,1,0.997778,0.999722,0.995833,1,0.9975,1] area_under_roc_curve 0.9977777777777779 [0.999167,1,0.999444,0.9875,0.999444,0.995278,0.998333,1,0.998611,1] area_under_roc_curve 0.9964166666666666 [1,0.998056,1,0.998889,0.997778,0.999444,0.976389,0.999722,0.995833,0.998056] 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 0.9595773204117234 [0.95,0.894737,1,0.97561,0.97561,0.95,0.974359,1,0.923077,0.952381] f_measure 0.9401466517348536 [1,0.883721,1,0.9,0.888889,0.923077,0.930233,0.926829,0.974359,0.974359] f_measure 0.9655213485701291 [0.974359,0.974359,1,0.974359,0.97561,0.923077,1,0.974359,0.95,0.909091] f_measure 0.9550250378157356 [0.974359,0.930233,0.952381,0.918919,0.974359,0.95,1,0.95,0.95,0.95] f_measure 0.9258610805979227 [0.952381,0.863636,0.974359,0.923077,0.894737,0.857143,1,0.974359,0.918919,0.9] f_measure 0.9600785735445555 [1,0.97561,1,0.974359,0.95,0.904762,0.947368,0.97561,0.95,0.923077] f_measure 0.9551584248810866 [0.974359,0.947368,0.974359,0.930233,0.97561,0.923077,1,0.952381,0.926829,0.947368] f_measure 0.964579883468142 [0.952381,0.974359,0.97561,1,0.930233,0.974359,0.974359,1,0.888889,0.97561] f_measure 0.945169654906497 [0.95,0.974359,0.95,0.904762,0.952381,0.923077,0.947368,0.952381,0.95,0.947368] f_measure 0.9449633699633699 [0.974359,0.952381,1,0.95,0.974359,0.952381,0.871795,0.974359,0.9,0.9] kappa 0.9555555555555555 kappa 0.9333333333333332 kappa 0.961111111111111 kappa 0.95 kappa 0.9166666666666667 kappa 0.9555555555555555 kappa 0.95 kappa 0.961111111111111 kappa 0.9388888888888889 kappa 0.9388888888888889 kb_relative_information_score 0.9521897532920002 kb_relative_information_score 0.9374785862854496 kb_relative_information_score 0.954095263578571 kb_relative_information_score 0.9536358311552362 kb_relative_information_score 0.9180622789417093 kb_relative_information_score 0.9529636657517753 kb_relative_information_score 0.9518386580029735 kb_relative_information_score 0.9597768520345628 kb_relative_information_score 0.9489516148813312 kb_relative_information_score 0.943855479248739 mean_absolute_error 0.011355099863017163 mean_absolute_error 0.015402299011114104 mean_absolute_error 0.01153836008283019 mean_absolute_error 0.010694936157503993 mean_absolute_error 0.021230070791644247 mean_absolute_error 0.010593334836606188 mean_absolute_error 0.011209666730440371 mean_absolute_error 0.010410650691101838 mean_absolute_error 0.012410126233058205 mean_absolute_error 0.012404264429815475 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 0.9605665679349891 [0.95,0.944444,1,0.952381,0.952381,0.95,1,1,0.947368,0.909091] precision 0.944778249972758 [1,0.826087,1,0.9,1,0.947368,0.869565,0.904762,1,1] precision 0.9683082706766918 [1,1,1,1,0.952381,0.947368,1,1,0.95,0.833333] precision 0.9578656126482212 [1,0.869565,0.909091,1,1,0.95,1,0.95,0.95,0.95] precision 0.931075225943647 [0.909091,0.791667,1,0.947368,0.944444,0.818182,1,1,1,0.9] precision 0.96157666894509 [1,0.952381,1,1,0.95,0.863636,1,0.952381,0.95,0.947368] precision 0.9583167404677703 [1,1,1,0.869565,0.952381,0.947368,1,0.909091,0.904762,1] precision 0.9683418031244118 [0.909091,1,0.952381,1,0.869565,1,1,1,1,0.952381] precision 0.9479186602870814 [0.95,1,0.95,0.863636,0.909091,0.947368,1,0.909091,0.95,1] precision 0.9462918660287082 [1,0.909091,1,0.95,1,0.909091,0.894737,1,0.9,0.9] predictive_accuracy 0.96 predictive_accuracy 0.94 predictive_accuracy 0.965 predictive_accuracy 0.955 predictive_accuracy 0.925 predictive_accuracy 0.96 predictive_accuracy 0.955 predictive_accuracy 0.965 predictive_accuracy 0.945 predictive_accuracy 0.945 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 0.0630838881278732 relative_absolute_error 0.0855683278395229 relative_absolute_error 0.0641020004601678 relative_absolute_error 0.05941631198613337 relative_absolute_error 0.11794483773135706 relative_absolute_error 0.05885186020336777 relative_absolute_error 0.06227592628022435 relative_absolute_error 0.05783694828389916 relative_absolute_error 0.06894514573921233 relative_absolute_error 0.0689125801656416 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 0.08609178543568423 root_mean_squared_error 0.09512595164002498 root_mean_squared_error 0.08416839794800114 root_mean_squared_error 0.08049326777992838 root_mean_squared_error 0.10506485628752883 root_mean_squared_error 0.08637119520476061 root_mean_squared_error 0.08454775372765388 root_mean_squared_error 0.07433711146184639 root_mean_squared_error 0.09031667635419985 root_mean_squared_error 0.0981163539549229 root_relative_squared_error 0.2869726181189476 root_relative_squared_error 0.31708650546675016 root_relative_squared_error 0.2805613264933373 root_relative_squared_error 0.2683108925997615 root_relative_squared_error 0.3502161876250963 root_relative_squared_error 0.28790398401586886 root_relative_squared_error 0.28182584575884645 root_relative_squared_error 0.24779037153948813 root_relative_squared_error 0.30105558784733305 root_relative_squared_error 0.32705451318307643 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 0.96 [0.95,0.85,1,1,1,0.95,0.95,1,0.9,1] unweighted_recall 0.9400000000000001 [1,0.95,1,0.9,0.8,0.9,1,0.95,0.95,0.95] unweighted_recall 0.9650000000000001 [0.95,0.95,1,0.95,1,0.9,1,0.95,0.95,1] unweighted_recall 0.9549999999999998 [0.95,1,1,0.85,0.95,0.95,1,0.95,0.95,0.95] unweighted_recall 0.925 [1,0.95,0.95,0.9,0.85,0.9,1,0.95,0.85,0.9] unweighted_recall 0.9600000000000002 [1,1,1,0.95,0.95,0.95,0.9,1,0.95,0.9] unweighted_recall 0.9550000000000001 [0.95,0.9,0.95,1,1,0.9,1,1,0.95,0.9] unweighted_recall 0.9650000000000001 [1,0.95,1,1,1,0.95,0.95,1,0.8,1] unweighted_recall 0.9450000000000001 [0.95,0.95,0.95,0.95,1,0.9,0.9,1,0.95,0.9] unweighted_recall 0.9450000000000001 [0.95,1,1,0.95,0.95,1,0.85,0.95,0.9,0.9] usercpu_time_millis 7252.209891000348 usercpu_time_millis 5147.134561999337 usercpu_time_millis 7339.891790000365 usercpu_time_millis 6420.927780000056 usercpu_time_millis 4541.327655999339 usercpu_time_millis 7068.9321879999625 usercpu_time_millis 7982.772502999978 usercpu_time_millis 6044.2999750002855 usercpu_time_millis 6387.452982999093 usercpu_time_millis 7604.426196001441 usercpu_time_millis_testing 2.9279000000315136 usercpu_time_millis_testing 2.775499999188469 usercpu_time_millis_testing 2.951800000118965 usercpu_time_millis_testing 3.4607000006872113 usercpu_time_millis_testing 3.3251999993808568 usercpu_time_millis_testing 2.811600000313774 usercpu_time_millis_testing 3.0029999998077983 usercpu_time_millis_testing 3.316100000120059 usercpu_time_millis_testing 3.4872009991886443 usercpu_time_millis_testing 2.932300000793475 usercpu_time_millis_training 7249.281991000316 usercpu_time_millis_training 5144.359062000149 usercpu_time_millis_training 7336.939990000246 usercpu_time_millis_training 6417.467079999369 usercpu_time_millis_training 4538.002455999958 usercpu_time_millis_training 7066.120587999649 usercpu_time_millis_training 7979.7695030001705 usercpu_time_millis_training 6040.983875000165 usercpu_time_millis_training 6383.965781999905 usercpu_time_millis_training 7601.4938960006475 wall_clock_time_millis 7258.0249309539795 wall_clock_time_millis 5157.337188720703 wall_clock_time_millis 7340.352296829224 wall_clock_time_millis 6429.428339004517 wall_clock_time_millis 4550.028324127197 wall_clock_time_millis 7082.944393157959 wall_clock_time_millis 7996.770858764648 wall_clock_time_millis 6049.736261367798 wall_clock_time_millis 6390.425205230713 wall_clock_time_millis 7621.835947036743 wall_clock_time_millis_testing 2.9327869415283203 wall_clock_time_millis_testing 2.7780532836914062 wall_clock_time_millis_testing 2.955198287963867 wall_clock_time_millis_testing 3.464221954345703 wall_clock_time_millis_testing 3.328084945678711 wall_clock_time_millis_testing 2.8150081634521484 wall_clock_time_millis_testing 3.0062198638916016 wall_clock_time_millis_testing 3.319978713989258 wall_clock_time_millis_testing 3.490447998046875 wall_clock_time_millis_testing 2.935647964477539 wall_clock_time_millis_training 7255.092144012451 wall_clock_time_millis_training 5154.559135437012 wall_clock_time_millis_training 7337.39709854126 wall_clock_time_millis_training 6425.964117050171 wall_clock_time_millis_training 4546.700239181519 wall_clock_time_millis_training 7080.129384994507 wall_clock_time_millis_training 7993.764638900757 wall_clock_time_millis_training 6046.416282653809 wall_clock_time_millis_training 6386.934757232666 wall_clock_time_millis_training 7618.900299072266 weighted_recall 0.96 [0.95,0.85,1,1,1,0.95,0.95,1,0.9,1] weighted_recall 0.94 [1,0.95,1,0.9,0.8,0.9,1,0.95,0.95,0.95] weighted_recall 0.965 [0.95,0.95,1,0.95,1,0.9,1,0.95,0.95,1] weighted_recall 0.955 [0.95,1,1,0.85,0.95,0.95,1,0.95,0.95,0.95] weighted_recall 0.925 [1,0.95,0.95,0.9,0.85,0.9,1,0.95,0.85,0.9] weighted_recall 0.96 [1,1,1,0.95,0.95,0.95,0.9,1,0.95,0.9] weighted_recall 0.955 [0.95,0.9,0.95,1,1,0.9,1,1,0.95,0.9] weighted_recall 0.965 [1,0.95,1,1,1,0.95,0.95,1,0.8,1] weighted_recall 0.945 [0.95,0.95,0.95,0.95,1,0.9,0.9,1,0.95,0.9] weighted_recall 0.945 [0.95,1,1,0.95,0.95,1,0.85,0.95,0.9,0.9]