10090038 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) 8015126 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 "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 20 8783 min_samples_split 13 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 58729 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 21100613 description https://api.openml.org/data/download/21100613/description.xml -1 21100614 predictions https://api.openml.org/data/download/21100614/predictions.arff area_under_roc_curve 0.8765133759469692 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0.01534429492829332 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 0.33 prior_entropy 6.6438561897747395 recall 0.33 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[0.45283,0.726266,0.477987,0.993671,0.993671,0.468553,0.966772,0.977987,0.993671,1,0.990566,0.996855,0.977848,0.987342,0.996835,0.481132,0.993711,0.943396,0.960443,0.984277,0.984177,0.97943,0.993711,0.993671,0.996835,0.45283,0.465409,0.974684,0.993671,0.949686,0.993671,0.996835,0.987421,1,0.982595,0.996835,0.742089,0.976266,0.481013,0.996855,0.471698,0.424051,0.990506,0.973101,0.735759,0.731013,0.993711,0.735759,0.971698,0.474843,0.947785,0.990506,0.735759,0.719937,1,0.943396,0.993671,0.481132,0.988924,0.993671,0.996835,0.990506,0.493671,0.968354,0.984277,0.965409,0.977848,0.937107,0.716772,0.930818,0.927673,0.996855,0.712025,0.458861,0.950949,0.987342,1,0.427673,0.993711,0.985759,0.713608,0.487421,0.990566,0.490566,0.993671,0.977848,0.734177,0.974684,0.996855,0.740506,0.993671,0.990506,0.971519,0.938291,0.996855,0.977848,0.455975,0.993671,0.707278,0.471698] 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 0.2737319913163607 kappa 0.34969615657801284 kappa 0.33714736634444664 kappa 0.34325295023088764 kappa 0.33051750680930014 kappa 0.29902115566782445 kappa 0.38067777865550206 kappa 0.3052540165002171 kappa 0.3116785728381418 kappa 0.29927007299270075 kb_relative_information_score 79.86003521219408 kb_relative_information_score 85.47794107278237 kb_relative_information_score 84.15993920575639 kb_relative_information_score 85.76632446174945 kb_relative_information_score 81.04689385788691 kb_relative_information_score 84.83571829851175 kb_relative_information_score 89.05315257333345 kb_relative_information_score 84.45649932756571 kb_relative_information_score 82.96036220704394 kb_relative_information_score 82.60524130296668 mean_absolute_error 0.015817634594886668 mean_absolute_error 0.015201921369779978 mean_absolute_error 0.015284175952897184 mean_absolute_error 0.015117343284236842 mean_absolute_error 0.015316915252945599 mean_absolute_error 0.01539970555411906 mean_absolute_error 0.014895460992074089 mean_absolute_error 0.015462345767905247 mean_absolute_error 0.015490552453688486 mean_absolute_error 0.015456894060400688 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 0.28125 predictive_accuracy 0.35625 predictive_accuracy 0.34375 predictive_accuracy 0.35 predictive_accuracy 0.3375 predictive_accuracy 0.30625 predictive_accuracy 0.3875 predictive_accuracy 0.3125 predictive_accuracy 0.31875 predictive_accuracy 0.30625 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 0.28125 [0,0,1,1,0,0,1,0,0,1,0,0.5,0,0,0,0,0.5,0,0,0,1,0,0,1,0,0,0,0,0,0,0.5,0.5,0.5,0,0.5,0,1,0,1,0,1,1,1,0,0,0,0,1,1,0.5,0,0,0,0,0,0,0,1,0,0,1,0,0.5,0.5,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,0,0,1,0,0.5,1,1,0,0,0.5,0,0,0,0,0.5,0,0,0,1] recall 0.35625 [0,0,1,1,1,0,0,0,1,1,0.5,0.5,0,0,0,1,0.5,0,0,0,1,1,0,0,1,0,0,0,1,0,0.5,0,0,0,0,0,0,1,0,1,1,1,1,0,0,0.5,1,0,0,1,1,0.5,0,0,0,0,1,0,0,0,1,0,0,0.5,1,0.5,0.5,0,0,0,0,1,0,0,0,0,0.5,0,1,0,0,0.5,0,1,0,1,0,1,0,1,1,1,0,0,0.5,1,0,0.5,0,0] recall 0.34375 [0,0,1,1,0.5,0,0,0,0.5,1,1,0.5,0,0,1,1,1,0,0,0.5,0,0,1,0,1,0,0,0,0.5,1,1,1,0,0,0,0,0,0,0,0.5,0.5,0,1,1,0,0,0.5,1,0.5,0.5,0.5,0,1,0,0,0.5,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,1,0,1,1,1,0,0,1,1,1,0,0,0,0,1,0,0,0,0.5] recall 0.35 [0,1,1,0,0.5,0,0,1,0,1,0.5,1,0,0,0,0.5,1,0,0,0,1,1,0.5,0,0,0,1,0,1,0,1,0.5,1,0.5,1,0,1,1,0,1,0.5,0,1,1,0,0,0.5,0,0,0,0,0,0,0,1,0,1,0,0,1,1,0,0,1,0.5,0,0,0.5,0,0.5,0,0.5,0,0,0,0,0,0,0.5,0,0,0.5,0,1,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1] recall 0.3375 [0,0,1,1,1,0,0,0,1,1,1,0,0,0,1,0,1,0.5,0,0,1,1,0,0,0,0,0,0,1,0.5,0,0,0,0,1,0,0.5,0,0.5,1,1,0.5,1,0,0,1,0.5,0,0,0,0,0,0.5,0,0,0,1,0,0,0,0.5,1,1,0,0.5,1,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,0,1,0,1,0,0,0,0,1,1,1,0.5,0,0,0,0,0.5,0,0,0] recall 0.30625 [0,0,1,0,1,0,0,0,1,1,0,1,0,0,0,1,1,0.5,0,0,0,1,0,1,0,0,0,0,1,0.5,0,0,0.5,0.5,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0.5,0.5,1,0,0.5,1,1,0,1,0,0,0,1,0,1,0,1,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.5,0,1,0,1,0,0,0,0.5,0,0.5,0,0,0.5,1,0,0,0,0] recall 0.3875 [0.5,0,0,0.5,1,1,0,0,0,1,1,1,1,0,0,0,1,1,0,0.5,0.5,0,0,1,0,0,0,0.5,0,1,0.5,0,0,0,0.5,1,0,0,0,1,0,0,1,0,0,1,0.5,1,1,1,0,1,1,0,0,0.5,0,1,0,0,1,1,1,0,0.5,0,0,0,1,0,0,0,1,0,0.5,0,1,0,1,0,0,1,0.5,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0] recall 0.3125 [0,1,0,0.5,1,0,1,1,0,0,0,1,0,0,0,0.5,1,0,0,0,1,0,0.5,0,0.5,0,0,0,0,1,1,1,0,1,0,0,0.5,0,0,0,1,0.5,0,0.5,0,0.5,0,0,0,1,1,0,1,1,0.5,0,1,1,0,0,0.5,0,1,0,0,0,0,0,0,0,0,0,0.5,1,0,0,0,0,1,0,0,0,0.5,1,0.5,0,0.5,0,0,0,0,0.5,0,0,0,0.5,0,0,0,1] recall 0.31875 [0,0.5,1,1,0,1,0.5,0,0,1,0,1,1,0.5,0,0,1,0,1,1,1,0,0,0.5,0.5,0,0,0,1,0,0,1,0,1,0.5,0,0,0,0.5,0,1,0.5,0.5,0,0,1,0,1,0,1,0.5,0.5,0,0.5,0,0,0,0,0,0,1,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,0,0.5,0,0,0,0] recall 0.30625 [0,0,0,1,1,0,0.5,0,0,1,1,1,1,0,0,0,1,0,0,0,0.5,0,1,1,1,0,0,0,1,0,1,1,0,1,0.5,1,0.5,0,0,1,0,0,0.5,0,0,0.5,0,0.5,0,0,0,0,0,0.5,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,0.5,0.5,0,1,0.5,0,0,0,0,1,0,0,0,0,0] relative_absolute_error 0.798870434085184 relative_absolute_error 0.7677738065545431 relative_absolute_error 0.7719280784291495 relative_absolute_error 0.7635021860725665 relative_absolute_error 0.7735815784315946 relative_absolute_error 0.7777629067736885 relative_absolute_error 0.7522960097007102 relative_absolute_error 0.7809265539346072 relative_absolute_error 0.7823511340246698 relative_absolute_error 0.7806512151717506 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 0.09361076194594034 root_mean_squared_error 0.09034830315067796 root_mean_squared_error 0.09130695748125563 root_mean_squared_error 0.09016213073853928 root_mean_squared_error 0.09218367071566053 root_mean_squared_error 0.09115786633691737 root_mean_squared_error 0.0889341927813471 root_mean_squared_error 0.09082383815137672 root_mean_squared_error 0.09212905466428575 root_mean_squared_error 0.09225216600040236 root_relative_squared_error 0.9408235567089801 root_relative_squared_error 0.908034612109343 root_relative_squared_error 0.9176694506492687 root_relative_squared_error 0.906163508965769 root_relative_squared_error 0.9264807501864201 root_relative_squared_error 0.9161710282694665 root_relative_squared_error 0.8938222681480661 root_relative_squared_error 0.9128139186911587 root_relative_squared_error 0.9259318382169022 root_relative_squared_error 0.9271691536997448 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 51.92308899950149 usercpu_time_millis 44.57047599998987 usercpu_time_millis 48.817581000093924 usercpu_time_millis 46.17518699978973 usercpu_time_millis 44.29505300004166 usercpu_time_millis 42.868860000453424 usercpu_time_millis 41.17992000055892 usercpu_time_millis 37.67670100023679 usercpu_time_millis 36.80356599988954 usercpu_time_millis 35.878688999673614 usercpu_time_millis_testing 1.7501329998594883 usercpu_time_millis_testing 1.6768800001045747 usercpu_time_millis_testing 1.6773140000623243 usercpu_time_millis_testing 1.7417509998267633 usercpu_time_millis_testing 1.6529810000065481 usercpu_time_millis_testing 1.6352550001101918 usercpu_time_millis_testing 1.3207230003899895 usercpu_time_millis_testing 1.4045630000509846 usercpu_time_millis_testing 1.3121759998284688 usercpu_time_millis_testing 1.2457319999157335 usercpu_time_millis_training 50.172955999642 usercpu_time_millis_training 42.89359599988529 usercpu_time_millis_training 47.1402670000316 usercpu_time_millis_training 44.43343599996297 usercpu_time_millis_training 42.642072000035114 usercpu_time_millis_training 41.23360500034323 usercpu_time_millis_training 39.85919700016893 usercpu_time_millis_training 36.272138000185805 usercpu_time_millis_training 35.491390000061074 usercpu_time_millis_training 34.63295699975788