10111122 1 Jan van Rijn 9954 Supervised Classification 8815 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1) 8036394 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "median" 8778 verbose 0 8778 copy true 8779 with_mean true 8779 with_std true 8779 memory null 8780 copy true 8781 fill_value -1 8781 missing_values NaN 8781 strategy "constant" 8781 verbose 0 8781 categorical_features null 8782 categories null 8782 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 8782 handle_unknown "ignore" 8782 n_values null 8782 sparse true 8782 class_weight null 8783 criterion "gini" 8783 max_depth null 8783 max_features 1.0 8783 max_leaf_nodes null 8783 min_impurity_decrease 0.0 8783 min_impurity_split null 8783 min_samples_leaf 18 8783 min_samples_split 6 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 8007 8783 splitter "best" 8783 n_jobs null 8812 remainder "passthrough" 8812 sparse_threshold 0.3 8812 transformer_weights null 8812 memory null 8813 memory null 8815 threshold 0.0 8816 openml-python Sklearn_0.20.0. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21142782 description https://api.openml.org/data/download/21142782/description.xml -1 21142783 predictions https://api.openml.org/data/download/21142783/predictions.arff area_under_roc_curve 0.8715297506313129 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0.014965602425133236 mean_prior_absolute_error 0.019800000000000036 number_of_instances 1600 [16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16] predictive_accuracy 0.355 prior_entropy 6.6438561897747395 recall 0.355 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[0.45283,0.738924,0.477987,0.996835,0.993671,0.996855,0.988924,0.959119,0.985759,1,0.990566,0.996855,0.990506,0.987342,0.996835,0.481132,0.996855,0.955975,0.969937,1,0.484177,0.97943,0.993711,0.993671,0.996835,0.918239,0.471698,0.960443,0.993671,0.474843,0.996835,0.996835,0.996855,0.993711,0.97943,0.987342,0.740506,0.974684,0.487342,0.996855,0.471698,0.43038,0.993671,0.966772,0.740506,0.734177,0.996855,0.735759,0.977987,0.481132,0.933544,0.987342,0.737342,0.724684,1,0.959119,0.993671,0.487421,0.988924,0.993671,0.996835,0.996835,0.493671,0.968354,0.996855,0.968553,0.973101,0.937107,0.724684,0.930818,0.924528,0.996855,0.716772,0.71519,0.938291,0.971519,1,0.446541,0.993711,0.985759,0.726266,0.487421,0.990566,0.490566,0.993671,0.977848,0.740506,0.974684,0.996855,0.740506,0.987342,0.990506,0.955696,0.981013,0.996855,0.97943,0.45283,0.993671,0.721519,0.996855] 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 0.29915946489878065 kappa 0.368661957937103 kappa 0.36896075724709126 kappa 0.3242549832247878 kappa 0.3432788696819007 kappa 0.36851245214508427 kappa 0.3999921051592784 kappa 0.3432788696819007 kappa 0.3120586959094316 kappa 0.356035197095845 kb_relative_information_score 81.27014863699098 kb_relative_information_score 84.83155316548029 kb_relative_information_score 85.6897967224845 kb_relative_information_score 81.74808308686075 kb_relative_information_score 82.3091971818556 kb_relative_information_score 82.55690562915069 kb_relative_information_score 91.23438492974078 kb_relative_information_score 86.7485805617555 kb_relative_information_score 83.71691992295695 kb_relative_information_score 86.05218981017865 mean_absolute_error 0.015421817642489963 mean_absolute_error 0.014969343281247702 mean_absolute_error 0.014697941413570448 mean_absolute_error 0.015207280015808154 mean_absolute_error 0.01499619412814931 mean_absolute_error 0.015190699135081068 mean_absolute_error 0.014344930143514697 mean_absolute_error 0.014894829370739441 mean_absolute_error 0.015206376341648916 mean_absolute_error 0.01472661277908314 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] predictive_accuracy 0.30625 predictive_accuracy 0.375 predictive_accuracy 0.375 predictive_accuracy 0.33125 predictive_accuracy 0.35 predictive_accuracy 0.375 predictive_accuracy 0.40625 predictive_accuracy 0.35 predictive_accuracy 0.31875 predictive_accuracy 0.3625 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 recall 0.30625 [0,0,1,1,0,0,1,0,0,1,0,0.5,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0.5,0.5,0.5,0,0.5,0,1,1,1,0,1,1,1,0,0,0,0,1,1,0.5,0,0,0,0,0,0,0,1,0,0,1,0,0,0.5,1,0.5,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,1,1,0,0.5,1,1,0,0,1,0,0,0,0,0.5,0,0,0,1] recall 0.375 [0,0,1,1,1,0,0,1,0,1,0.5,0.5,0.5,0,0,1,0.5,0,0,1,1,1,0,0,1,0,0,0,1,0,0,0,0.5,0.5,0,0,0,0,0,1,1,1,1,0,0,0.5,1,1,0.5,1,1,0.5,0,0,0,0,1,0,0,0,1,0,0,0.5,1,0,1,0,0,0,0,1,0,0,0,0,0.5,0,1,0,0,0.5,0,1,0,0,0,1,1,1,0.5,1,0,0,0.5,1,0,0.5,0,1] recall 0.375 [0,0,1,1,1,0,0,0,0.5,1,1,0,0,0,1,0.5,1,0,0,0,1,0,1,1,1,0,0,0,0.5,1,0,1,0,0,1,1,1,0,0,0.5,0.5,0,1,1,0,0,0.5,1,0.5,0.5,0.5,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0.5,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0.5,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0,0,0,0.5] recall 0.33125 [0,1,1,0,0.5,0.5,0,1,0,1,0.5,1,0,0,0.5,0.5,1,0,0,0,1,1,0.5,0,0,0,0,1,1,0,0,0.5,0.5,0,0,1,0,1,0,1,0,1,1,1,0,0,0.5,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0.5,0.5,0.5,0,0,0.5,0,0.5,0,0,0,0,0.5,0,0.5,0,0,0.5,0.5,1,0,1,0,0,0,0,0.5,0,0,0,0.5,0,0,0,1,1] recall 0.35 [0.5,1,1,0.5,1,0,0,0,0,1,0.5,0,0,0,1,0.5,1,0.5,0,0,1,1,0,1,0,0,0,0,1,0.5,1,0,0,0,0,0,0.5,0,0.5,1,1,1,1,0,0,1,1,1,0,0.5,0,0,0.5,0,0,0,1,0,0,0,1,1,1,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0] recall 0.375 [0,0,0,0.5,0,0.5,1,0,1,0.5,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0.5,0,0,0,0,0.5,1,1,0,0.5,1,1,0.5,0.5,0,0,1,0.5,0,0,0,0,0.5,0.5,0,0.5,1,1,0.5,0,0.5,0,1,1,0,0,0,1,1,1,0.5,0.5,1,0,0,0,0,0,1,1,0.5,0,1,0,1,0,0,0.5,0.5,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0] recall 0.40625 [1,0,0,0.5,1,1,0.5,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,0,1,0.5,0,0,0,0,1,1,0,1,0,1,1,0,0.5,0,0,0.5,0,1,0,0,1,0.5,1,1,1,0,0.5,0.5,0,0,1,0,1,0,1,0,1,1,0,0.5,0,0,0,0,0,0,0,0.5,0,0,0,1,1,1,0,0,1,1,1,1,0,1,0,0,0.5,0,0,0,0,0,0.5,0,0,0,1] recall 0.35 [0,1,0,0.5,1,0,1,0.5,0,1,0,1,0,0,0,0.5,1,0,0,0,0,0,0.5,0,0.5,0,0,0,0,1,1,1,0,1,0,0,1,0,0,1,1,0.5,0,0,0,0.5,0,0,0,1,1,0,1,1,0.5,0.5,1,1,0,0,0.5,0,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0.5,1,0.5,0,0.5,0,0,0.5,0,0.5,0,0,0,1,0,0,0.5,1] recall 0.31875 [0,1,1,1,0,0,0.5,1,0.5,1,0,1,1,0.5,0,1,1,1,0,0,1,0,0,0.5,0.5,0,0,0,0.5,1,0,1,0,1,0.5,0,0,0,0,0,1,0.5,0.5,0,0,0.5,1,1,0,0,0.5,0.5,0,0,0,0,0,0,0,0,1,0.5,0,0.5,0,1,0,0,0,0,0,0,0.5,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0.5,0,0,1,0,0,0,0,0,0,0,0,0] recall 0.3625 [0,0.5,0,1,1,1,0.5,0,0,1,1,1,1,0,0,0,1,0,0,0,0,0,1,1,1,0,0,0,1,0,1,1,1,1,0.5,0,0.5,0,0,1,0,0,0.5,0,0.5,0.5,0,0.5,0,0,0,0.5,0,0.5,1,0,0,0,0,1,1,1,0,0,1,0,0.5,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0.5,0.5,0,1,0.5,0.5,0,0,0,1,0.5,0,0,0,1] relative_absolute_error 0.7788796789136332 relative_absolute_error 0.7560274384468524 relative_absolute_error 0.7423202734126477 relative_absolute_error 0.7680444452428347 relative_absolute_error 0.7573835418257214 relative_absolute_error 0.7672070270242951 relative_absolute_error 0.72449142138963 relative_absolute_error 0.7522641096333039 relative_absolute_error 0.7679988051337824 relative_absolute_error 0.7437683221759148 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_squared_error 0.09272816370330988 root_mean_squared_error 0.09015590489708873 root_mean_squared_error 0.08989916542920849 root_mean_squared_error 0.09200615054984743 root_mean_squared_error 0.09138162128700143 root_mean_squared_error 0.09124117603483087 root_mean_squared_error 0.08701515408890928 root_mean_squared_error 0.08976169980362463 root_mean_squared_error 0.09162001945349332 root_mean_squared_error 0.09118680748364184 root_relative_squared_error 0.9319531106137308 root_relative_squared_error 0.906100936904873 root_relative_squared_error 0.9035206081659812 root_relative_squared_error 0.9246966053902875 root_relative_squared_error 0.9184198501313258 root_relative_squared_error 0.9170083222372755 root_relative_squared_error 0.8745352035996103 root_relative_squared_error 0.9021390266458807 root_relative_squared_error 0.920815841855454 root_relative_squared_error 0.9164618977382172 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 68.13379500090377 usercpu_time_millis 54.545118000532966 usercpu_time_millis 60.4365390008752 usercpu_time_millis 54.790696000054595 usercpu_time_millis 41.20362799949362 usercpu_time_millis 42.33771899998828 usercpu_time_millis 41.64571599903866 usercpu_time_millis 40.844199998900876 usercpu_time_millis 43.73412799941434 usercpu_time_millis 41.79741600091802 usercpu_time_millis_testing 1.8070920013997238 usercpu_time_millis_testing 1.7191890001413412 usercpu_time_millis_testing 1.7179860005853698 usercpu_time_millis_testing 1.7638259996601846 usercpu_time_millis_testing 1.2972589993296424 usercpu_time_millis_testing 1.3602380004158476 usercpu_time_millis_testing 1.3918929998908425 usercpu_time_millis_testing 1.2844929988204967 usercpu_time_millis_testing 1.334524999037967 usercpu_time_millis_testing 1.2953550012753112 usercpu_time_millis_training 66.32670299950405 usercpu_time_millis_training 52.825929000391625 usercpu_time_millis_training 58.71855300028983 usercpu_time_millis_training 53.02687000039441 usercpu_time_millis_training 39.90636900016398 usercpu_time_millis_training 40.977480999572435 usercpu_time_millis_training 40.253822999147815 usercpu_time_millis_training 39.55970700008038 usercpu_time_millis_training 42.39960300037637 usercpu_time_millis_training 40.50206099964271