10087321 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) 8012389 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "mean" 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 "entropy" 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 2 8783 min_samples_split 7 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 25859 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 21095178 description https://api.openml.org/data/download/21095178/description.xml -1 21095179 predictions https://api.openml.org/data/download/21095179/predictions.arff area_under_roc_curve 0.7623845880681819 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[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] precision 0.4864565542256014 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[0.5,0.6875,0.75,0.6875,0.75,0.5,0.5,0.5625,0.5,0.6875,0.5,0.5,0.25,0.4375,0.75,0.3125,0.8125,0.125,0.375,0.375,0.375,0.6875,0.5,0.9375,0.5,0.0625,0.25,0.625,0.5625,0.5625,0.625,0.75,0.5,0.125,0.5625,0.5625,0.4375,0.1875,0.875,0.6875,0.4375,0.625,0.6875,0.5,0.625,0.375,0.5,0.375,0.3125,0.625,0.5,0.3125,0.5,0.1875,0.4375,0.1875,0.8125,0.5625,0.3125,0.3125,0.5,0.4375,0.375,0.25,0.625,0.375,0.1875,0.75,0.25,0.1875,0.25,0.5,0.375,0.5625,0,0.5625,0.6875,0.125,0.625,0.5625,0.625,0.1875,0.3125,0.3125,0.4375,0.5,0.5625,0.3125,0.375,0.5625,0.625,0.75,0.4375,0.1875,0.625,0.4375,0.375,0.3125,0.0625,0.375] relative_absolute_error 0.5472011784511701 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09453304463402058 root_relative_squared_error 0.9500928464877755 total_cost 0 area_under_roc_curve 0.7653749701456886 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[0.487421,0.496835,0.5,0.746835,1,0.996855,0.743671,0.496855,0.742089,1,0.990566,0.5,0.496835,0.748418,0.996835,0.993711,1,0.5,0.745253,1,0.75,0.996835,0.496855,1,1,0.490566,1,0.746835,0.493671,0.996855,0.5,0.75,1,0.484277,0.995253,0.75,0.487342,0.740506,0.996835,0.5,0.993711,0.993671,0.75,0.746835,1,0.745253,0.5,0.5,0.490566,0.496855,0.745253,0.496835,0.996835,0.484177,0.748418,0.487421,1,0.496855,0.493671,0.493671,0.996835,0.746835,0.487342,0.75,0.996855,0.5,0.734177,0.996855,0.998418,0.496855,0.493711,1,0.496835,0.996835,0.484177,0.998418,1,0.490566,0.496855,0.996835,0.996835,0.490566,1,0.5,0.998418,1,0.998418,0.988924,0.484277,0.996835,1,0.996835,1,0.737342,1,1,0.496855,0.5,0.745253,1] 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 0.48855564325177586 kappa 0.4191002367797948 kappa 0.4378873406229029 kappa 0.48224151539068666 kappa 0.39386764531786433 kappa 0.45047570170936796 kappa 0.5010066716671272 kappa 0.4506058333662233 kappa 0.50726468730259 kappa 0.4819962097283639 kb_relative_information_score 82.82280721158286 kb_relative_information_score 75.76640776719141 kb_relative_information_score 74.8808148308604 kb_relative_information_score 85.32240237923219 kb_relative_information_score 70.0152678368664 kb_relative_information_score 79.42319991345175 kb_relative_information_score 87.82921964701123 kb_relative_information_score 79.8267916764185 kb_relative_information_score 89.02767453810507 kb_relative_information_score 85.05390068524625 mean_absolute_error 0.010318750000000005 mean_absolute_error 0.011422916666666671 mean_absolute_error 0.011391666666666677 mean_absolute_error 0.010602083333333337 mean_absolute_error 0.011877083333333342 mean_absolute_error 0.011056250000000007 mean_absolute_error 0.010275000000000005 mean_absolute_error 0.011179166666666674 mean_absolute_error 0.009922916666666674 mean_absolute_error 0.010300000000000007 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 0.49375 predictive_accuracy 0.425 predictive_accuracy 0.44375 predictive_accuracy 0.4875 predictive_accuracy 0.4 predictive_accuracy 0.45625 predictive_accuracy 0.50625 predictive_accuracy 0.45625 predictive_accuracy 0.5125 predictive_accuracy 0.4875 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 0.49375 [1,0.5,1,0,0.5,1,0,1,0.5,1,1,0.5,0,0.5,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0.5,0,1,1,0.5,0,1,0,0,0,1,1,0,0,0.5,1,1,0.5,0,1,0,0.5,0.5,0,1,0.5,1,0,0.5,1,1,0.5,0,1,0.5,0,1,0,0,1,0,0,0,1,0.5,0.5,0,1,0.5,0.5,0.5,1,1,0,1,1,0.5,0.5,1,0.5,0.5,0,0,1,1,0.5,0.5,0,1,0,0,1] recall 0.425 [1,1,0.5,1,1,1,0,1,0.5,1,0,0.5,0,0.5,1,1,1,0,0,1,1,0,1,1,1,0,0,0,0.5,0,0.5,1,0,0,0,1,0,0,1,1,0,1,1,0.5,0.5,0.5,1,0,0,1,0,0,1,0.5,1,0,0,1,1,0,1,1,0.5,0,1,0.5,0,1,0,0,0.5,1,0.5,0.5,0,0.5,0.5,0,0.5,0.5,0.5,0,0,0,0.5,0,0.5,0,0,0,0.5,1,0.5,0,0.5,0.5,1,0,0,0] recall 0.44375 [0,0.5,0.5,1,0.5,0.5,0,1,1,0.5,0.5,0.5,0,0,0,0,1,0,0,0,0,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,0.5,0,1,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,0.5,1,0,0,0,0,1,0.5,1,0,0,0,0,1,0.5,0.5,0,0.5,0,0,0.5,0.5,0,0.5,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5] recall 0.4875 [0.5,1,1,1,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0.5,1,0.5,0.5,0.5,0,0,0,1,0,1,0,0,0.5,1,0.5,0.5,1,1,0.5,0,1,0,1,0,1,1,0.5,1,1,0,1,0,0.5,1,1,1,1,1,0,0,1,0.5,1,1,0,1,1,0,0.5,0,0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0.5,0,1,0.5,0,0,1,0,1,0,1,0,0,1,0.5,0,0,0,1,0,0,0.5,0,0] recall 0.4 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,0.5,0.5,1,0,1,0,0.5,0.5,1,0.5,0,0.5,0.5,1,1,0,0,1,1,0,0,0,0,0,0,1,0.5,0,0.5,1,0.5,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0,0,1,0,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,1,0,1,1,0,0,0,1,0,0,1,0.5,1,1,0,1,0.5,1,0.5,1,0,0] recall 0.45625 [0.5,1,1,1,1,0,1,0.5,1,0.5,1,0.5,0,1,1,0.5,1,0,0,0,0.5,0.5,0.5,1,0.5,0,0.5,1,1,0.5,1,1,0.5,0,0,0,0,0.5,1,0,1,0.5,1,1,0,1,1,0,0,0.5,1,1,0.5,0,0.5,0,1,1,0,1,0.5,0,0,0,0.5,0.5,0,1,0.5,0.5,0,0.5,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,1,1,0.5,0,0,1,0,0.5,0,0,0.5] recall 0.50625 [1,0,1,0.5,1,1,1,1,0,0.5,0,1,0.5,1,0,0,0,0,0,1,0.5,1,1,1,0,0,0,0.5,0,1,1,0,1,0,1,0,1,0.5,0.5,0,0.5,1,0.5,0.5,1,0.5,0,0.5,0,1,1,0.5,0.5,0,0.5,0.5,1,0,0,0,1,0.5,1,0,0.5,1,0,1,0,0,1,1,0,0,0,1,1,1,1,1,0.5,0,0.5,0.5,0.5,0,0,0,1,0,1,1,0,0,0,0.5,1,0,0.5,0.5] recall 0.45625 [0.5,1,1,0,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0.5,0.5,0.5,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,1,1,1,0.5,1,0,1,0.5,0.5,1,1,0,1,0,0,1,0.5,0.5,1,1,0.5,1,0,0.5,1,0.5,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,1,0,1,0,0,0,0,0.5,0,0.5,1,0,1,0.5,0,1,0,0,0,1,0,0,0,0.5] recall 0.5125 [0,1,1,1,1,1,0.5,0,0.5,1,0,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,0,0,0.5,0.5,1,1,0.5,0,0,1,1,0.5,0,1,0,0,0.5,0.5,0,0,0,1,0,0,1,0,0.5,1,0,0,0,1,0,0,0,0,1,0,0.5,1,0,0.5,0,0.5,1,1,0,0.5,1,0,0,1,0,1,0.5,1,1,0,0,0.5,1,0.5,0.5,0,0.5,1,0,1,0,1,0,0,1,0,0] recall 0.4875 [0,0,0,0.5,1,1,0.5,0,0.5,1,1,0,0,0.5,1,1,1,0,0.5,1,0,1,0,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,1,0,0,0.5,0.5,0.5,1,0.5,0,0,0,0,0.5,0,1,0,0.5,0,1,0,0,0,1,0,0,0.5,1,0,0.5,1,1,0,0,0,0,1,0,1,1,0,0,0.5,1,0,1,0,1,1,0.5,0.5,0,1,1,1,1,0.5,1,0.5,0,0,0,1] relative_absolute_error 0.5211489898989893 relative_absolute_error 0.5769149831649825 relative_absolute_error 0.5753367003366999 relative_absolute_error 0.5354587542087534 relative_absolute_error 0.5998526936026931 relative_absolute_error 0.5583964646464641 relative_absolute_error 0.5189393939393934 relative_absolute_error 0.5646043771043765 relative_absolute_error 0.5011574074074069 relative_absolute_error 0.5202020202020197 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 0.09382811560389442 root_mean_squared_error 0.0962319607915051 root_mean_squared_error 0.09825332196815424 root_mean_squared_error 0.09280692323312954 root_mean_squared_error 0.10032568492442778 root_mean_squared_error 0.09654086641878086 root_mean_squared_error 0.09110338297170358 root_mean_squared_error 0.09603005171993471 root_mean_squared_error 0.08879107030927529 root_mean_squared_error 0.09079089222554822 root_relative_squared_error 0.9430080431642678 root_relative_squared_error 0.9671675963200438 root_relative_squared_error 0.9874830405283361 root_relative_squared_error 0.9327446736715386 root_relative_squared_error 1.0083110719083088 root_relative_squared_error 0.9702722146876289 root_relative_squared_error 0.915623449846042 root_relative_squared_error 0.9651383337983226 root_relative_squared_error 0.8923838331816107 root_relative_squared_error 0.9124827996779952 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 230.9949009977572 usercpu_time_millis 225.0546850009414 usercpu_time_millis 173.00581900053658 usercpu_time_millis 167.22179199859966 usercpu_time_millis 163.88266300054966 usercpu_time_millis 167.66995999932988 usercpu_time_millis 169.19600699839066 usercpu_time_millis 166.5122220001649 usercpu_time_millis 165.55197900015628 usercpu_time_millis 167.34407999683754 usercpu_time_millis_testing 1.7088479980884586 usercpu_time_millis_testing 1.7118559990194626 usercpu_time_millis_testing 1.3036720010859426 usercpu_time_millis_testing 1.329017999523785 usercpu_time_millis_testing 1.3478869987011421 usercpu_time_millis_testing 1.5426479985762853 usercpu_time_millis_testing 1.3338659991859458 usercpu_time_millis_testing 1.3066069986962248 usercpu_time_millis_testing 1.3057899996056221 usercpu_time_millis_testing 1.2885029973404016 usercpu_time_millis_training 229.28605299966875 usercpu_time_millis_training 223.34282900192193 usercpu_time_millis_training 171.70214699945063 usercpu_time_millis_training 165.89277399907587 usercpu_time_millis_training 162.5347760018485 usercpu_time_millis_training 166.1273120007536 usercpu_time_millis_training 167.8621409992047 usercpu_time_millis_training 165.2056150014687 usercpu_time_millis_training 164.24618900055066 usercpu_time_millis_training 166.05557699949713