10437782 11497 Fares Gaaloul 2272 Supervised Classification predictive_accuracy 17579 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(2) 8260895 memory null 17579 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "adaboostclassifier", "step_name": "adaboostclassifier"}}] 17579 n_jobs null 17580 remainder "passthrough" 17580 sparse_threshold 0.3 17580 transformer_weights null 17580 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] 17580 memory null 17581 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "missingindicator", "step_name": "missingindicator"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 17581 error_on_new false 17582 features "missing-only" 17582 missing_values NaN 17582 sparse "auto" 17582 axis 0 17583 copy true 17583 missing_values "NaN" 17583 strategy "median" 17583 verbose 0 17583 copy true 17584 with_mean true 17584 with_std true 17584 memory null 17585 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] 17585 copy true 17586 fill_value -1 17586 missing_values NaN 17586 strategy "constant" 17586 verbose 0 17586 categorical_features null 17587 categories null 17587 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 17587 handle_unknown "ignore" 17587 n_values null 17587 sparse true 17587 algorithm "SAMME" 17588 learning_rate 0.8258296169139974 17588 n_estimators 429 17588 random_state 53944 17588 class_weight null 17589 criterion "gini" 17589 max_depth 3 17589 max_features null 17589 max_leaf_nodes null 17589 min_impurity_decrease 0.0 17589 min_impurity_split null 17589 min_samples_leaf 1 17589 min_samples_split 2 17589 min_weight_fraction_leaf 0.0 17589 presort false 17589 random_state 16546 17589 splitter "best" 17589 openml-python Sklearn_0.20.0. 275 meta_all.arff https://www.openml.org/data/download/12453/meta_all.arff -1 21799917 description https://api.openml.org/data/download/21799917/description.xml -1 21799918 predictions https://api.openml.org/data/download/21799918/predictions.arff area_under_roc_curve 0.7611968937389344 [1,1,0.835821,0.762726,0.790909,0.596721] average_cost 0 f_measure 0.6330690022487869 [0.8,1,0.285714,0.8,0.470588,0.142857] kappa 0.39225902493487164 kb_relative_information_score -0.15265145713998649 mean_absolute_error 0.27530023542989784 mean_prior_absolute_error 0.20651179806109396 weighted_recall 0.676056338028169 [1,1,0.25,0.904762,0.363636,0.1] number_of_instances 71 [2,2,4,42,11,10] precision 0.6283550358756312 [0.666667,1,0.333333,0.716981,0.666667,0.25] predictive_accuracy 0.676056338028169 prior_entropy 1.7941163185226785 relative_absolute_error 1.3330968884811787 root_mean_prior_squared_error 0.31698470376062077 root_mean_squared_error 0.3693780721912992 root_relative_squared_error 1.1652867403666414 total_cost 0 unweighted_recall 0.6030663780663781 [1,1,0.25,0.904762,0.363636,0.1] area_under_roc_curve 0.75 [0.0,1,0.0,0.8,0.857143,0.142857] area_under_roc_curve 0.4523809523809524 [0.0,0.0,0.0,0.3,0.666667,1] area_under_roc_curve 0.5952380952380952 [0.0,0.0,0.0,0.416667,1,0.5] area_under_roc_curve 0.7857142857142857 [0.0,0.0,0.666667,0.916667,1,0.166667] area_under_roc_curve 0.9047619047619048 [0.0,0.0,1,0.916667,1,0.666667] area_under_roc_curve 0.7142857142857144 [0.0,0.0,1,0.583333,1,0.666667] area_under_roc_curve 0.8571428571428571 [0.0,0.0,1,0.75,1,1] area_under_roc_curve 0.9523809523809524 [1,0.0,0.0,1,0.666667,1] area_under_roc_curve 0.7142857142857143 [1,0.0,0.0,0.666667,0.666667,0.666667] area_under_roc_curve 0.7142857142857143 [0.0,1,0.0,0.75,0.166667,0.833333] 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 kappa 0.5151515151515151 kappa 0 kappa 0.22222222222222218 kappa 0.5333333333333333 kappa 0.7407407407407407 kappa -0.16666666666666674 kappa 0.4166666666666667 kappa 0.7407407407407407 kappa 0.22222222222222218 kappa 0.3225806451612902 kb_relative_information_score -0.13734036062455263 kb_relative_information_score -0.4246170232256318 kb_relative_information_score -0.2667065284313127 kb_relative_information_score -0.14601871199970037 kb_relative_information_score -0.13495523163209638 kb_relative_information_score -0.1428392759756045 kb_relative_information_score -0.13986178354932008 kb_relative_information_score -0.0726508434732036 kb_relative_information_score -0.07740165372537769 kb_relative_information_score -0.07657026268820118 mean_absolute_error 0.27539921420933944 mean_absolute_error 0.2752281628833792 mean_absolute_error 0.27578529029185933 mean_absolute_error 0.27571442355555514 mean_absolute_error 0.27454295257773476 mean_absolute_error 0.27566431968472077 mean_absolute_error 0.2753501929525122 mean_absolute_error 0.27466693889874655 mean_absolute_error 0.27532072516807293 mean_absolute_error 0.27531599425142317 mean_prior_absolute_error 0.20292207792207795 mean_prior_absolute_error 0.18614718614718617 mean_prior_absolute_error 0.20531849103277675 mean_prior_absolute_error 0.20964749536178107 mean_prior_absolute_error 0.20964749536178107 mean_prior_absolute_error 0.20964749536178107 mean_prior_absolute_error 0.20964749536178107 mean_prior_absolute_error 0.2108843537414966 mean_prior_absolute_error 0.2108843537414966 mean_prior_absolute_error 0.2108843537414966 number_of_instances 8 [0,1,0,5,1,1] number_of_instances 7 [0,0,0,5,1,1] number_of_instances 7 [0,0,0,4,2,1] number_of_instances 7 [0,0,1,4,1,1] number_of_instances 7 [0,0,1,4,1,1] number_of_instances 7 [0,0,1,4,1,1] number_of_instances 7 [0,0,1,4,1,1] number_of_instances 7 [1,0,0,4,1,1] number_of_instances 7 [1,0,0,4,1,1] number_of_instances 7 [0,1,0,4,1,1] predictive_accuracy 0.75 predictive_accuracy 0.7142857142857143 predictive_accuracy 0.5714285714285715 predictive_accuracy 0.7142857142857143 predictive_accuracy 0.8571428571428571 predictive_accuracy 0.42857142857142855 predictive_accuracy 0.7142857142857143 predictive_accuracy 0.8571428571428571 predictive_accuracy 0.5714285714285715 predictive_accuracy 0.5714285714285715 prior_entropy 1.796701491496139 prior_entropy 1.3845411274279098 prior_entropy 1.6475843065680442 prior_entropy 1.8280177931157293 prior_entropy 1.8280177931157293 prior_entropy 1.8280177931157293 prior_entropy 1.8280177931157293 prior_entropy 1.93329859228233 prior_entropy 1.93329859228233 prior_entropy 1.93329859228233 relative_absolute_error 1.3571673276236245 relative_absolute_error 1.478551293629316 relative_absolute_error 1.3432072722949897 relative_absolute_error 1.315133400853489 relative_absolute_error 1.3095455879592834 relative_absolute_error 1.3148944098235795 relative_absolute_error 1.3133960531097706 relative_absolute_error 1.3024529038101853 relative_absolute_error 1.3055531161195717 relative_absolute_error 1.3055306824180388 root_mean_prior_squared_error 0.31127091460525613 root_mean_prior_squared_error 0.2830453859441996 root_mean_prior_squared_error 0.31509680320481115 root_mean_prior_squared_error 0.3218928388748278 root_mean_prior_squared_error 0.3218928388748278 root_mean_prior_squared_error 0.3218928388748278 root_mean_prior_squared_error 0.3218928388748278 root_mean_prior_squared_error 0.32380836631966653 root_mean_prior_squared_error 0.32380836631966653 root_mean_prior_squared_error 0.32380836631966653 root_mean_squared_error 0.3695102053983379 root_mean_squared_error 0.3692804372748662 root_mean_squared_error 0.3700405196702173 root_mean_squared_error 0.3699382081421011 root_mean_squared_error 0.36835616281131034 root_mean_squared_error 0.36986772919048805 root_mean_squared_error 0.3694379881108707 root_mean_squared_error 0.3685215682740506 root_mean_squared_error 0.3694038854492122 root_mean_squared_error 0.3694013451265284 root_relative_squared_error 1.1871016148969107 root_relative_squared_error 1.3046686348304126 root_relative_squared_error 1.174370910483954 root_relative_squared_error 1.1492588944669142 root_relative_squared_error 1.144344074564984 root_relative_squared_error 1.1490399428684275 root_relative_squared_error 1.1477048989416365 root_relative_squared_error 1.1380853819886878 root_relative_squared_error 1.1408101947697464 root_relative_squared_error 1.1408023496275326 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 usercpu_time_millis 739.4951760000001 usercpu_time_millis 757.5445279999994 usercpu_time_millis 734.5652809999991 usercpu_time_millis 742.8959049999992 usercpu_time_millis 767.8657239999991 usercpu_time_millis 769.6143490000002 usercpu_time_millis 767.7174980000015 usercpu_time_millis 754.9397080000002 usercpu_time_millis 740.5854470000008 usercpu_time_millis 763.1127530000015 usercpu_time_millis_testing 21.17715099999984 usercpu_time_millis_testing 21.612088999999557 usercpu_time_millis_testing 20.82903899999966 usercpu_time_millis_testing 22.668885999999944 usercpu_time_millis_testing 22.19746199999939 usercpu_time_millis_testing 22.21639000000053 usercpu_time_millis_testing 22.81587200000068 usercpu_time_millis_testing 20.98108400000065 usercpu_time_millis_testing 21.805052999999575 usercpu_time_millis_testing 21.468241000000887 usercpu_time_millis_training 718.3180250000003 usercpu_time_millis_training 735.9324389999999 usercpu_time_millis_training 713.7362419999995 usercpu_time_millis_training 720.2270189999992 usercpu_time_millis_training 745.6682619999997 usercpu_time_millis_training 747.3979589999997 usercpu_time_millis_training 744.9016260000008 usercpu_time_millis_training 733.9586239999995 usercpu_time_millis_training 718.7803940000013 usercpu_time_millis_training 741.6445120000005 wall_clock_time_millis 742.0852184295654 wall_clock_time_millis 759.9906921386719 wall_clock_time_millis 735.4154586791992 wall_clock_time_millis 744.0676689147949 wall_clock_time_millis 774.5451927185059 wall_clock_time_millis 777.0376205444336 wall_clock_time_millis 773.5564708709717 wall_clock_time_millis 758.946418762207 wall_clock_time_millis 741.8041229248047 wall_clock_time_millis 768.5728073120117 wall_clock_time_millis_testing 20.994186401367188 wall_clock_time_millis_testing 21.71945571899414 wall_clock_time_millis_testing 20.837068557739258 wall_clock_time_millis_testing 22.876262664794922 wall_clock_time_millis_testing 22.539377212524414 wall_clock_time_millis_testing 22.340774536132812 wall_clock_time_millis_testing 23.081541061401367 wall_clock_time_millis_testing 20.98846435546875 wall_clock_time_millis_testing 21.904468536376953 wall_clock_time_millis_testing 21.544694900512695 wall_clock_time_millis_training 721.0910320281982 wall_clock_time_millis_training 738.2712364196777 wall_clock_time_millis_training 714.57839012146 wall_clock_time_millis_training 721.19140625 wall_clock_time_millis_training 752.0058155059814 wall_clock_time_millis_training 754.6968460083008 wall_clock_time_millis_training 750.4749298095703 wall_clock_time_millis_training 737.9579544067383 wall_clock_time_millis_training 719.8996543884277 wall_clock_time_millis_training 747.028112411499 weighted_recall 0.75 [0.0,1,0.0,1,0,0] weighted_recall 0.7142857142857143 [0.0,0.0,0.0,1,0,0] weighted_recall 0.5714285714285714 [0.0,0.0,0.0,0.75,0.5,0] weighted_recall 0.7142857142857143 [0.0,0.0,0,1,1,0] weighted_recall 0.8571428571428571 [0.0,0.0,1,1,1,0] weighted_recall 0.42857142857142855 [0.0,0.0,0,0.75,0,0] weighted_recall 0.7142857142857143 [0.0,0.0,0,1,1,0] weighted_recall 0.8571428571428571 [1,0.0,0.0,1,0,1] weighted_recall 0.5714285714285714 [1,0.0,0.0,0.75,0,0] weighted_recall 0.5714285714285714 [0.0,1,0.0,0.75,0,0]