10110072 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) 8035336 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 "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 20 8783 min_samples_split 12 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 52690 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 21140682 description https://api.openml.org/data/download/21140682/description.xml -1 21140683 predictions https://api.openml.org/data/download/21140683/predictions.arff area_under_roc_curve 0.8728028330176764 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0.015674760177109174 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 0.306875 prior_entropy 6.6438561897747395 recall 0.306875 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[0.940252,0.740506,0.490566,0.943038,0.993671,0.993711,0.704114,0.481132,0.735759,0.981132,0.990566,0.996855,0.990506,0.985759,0.996835,0.993711,0.993711,0.908805,0.982595,0.993711,0.996835,0.990506,0.996855,0.990506,1,0.871069,0.993711,0.72943,0.732595,0.990566,0.96519,0.743671,0.996855,0.965409,0.971519,0.724684,0.72943,0.993671,0.971519,1,0.955975,0.974684,0.990506,0.740506,0.990506,0.710443,0.987421,0.71519,0.990566,0.468553,0.990506,0.704114,0.993671,0.962025,0.734177,0.965409,0.996835,0.987421,0.458861,0.996835,0.987342,0.471519,0.996835,0.721519,0.990566,0.487421,0.962025,0.993711,0.981013,0.433962,0.45283,0.993711,0.981013,0.990506,0.710443,0.996835,1,1,0.468553,0.996835,0.993671,0.468553,0.987421,0.471698,0.976266,0.996835,0.954114,0.938291,0.433962,0.988924,1,0.993671,0.996835,0.977848,0.990566,0.993671,0.993711,0.981013,0.968354,0.977987] 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 0.34938807737860245 kappa 0.3306760329926201 kappa 0.3118143792912951 kappa 0.2736459813674404 kappa 0.3050894302522999 kappa 0.2741044658355689 kappa 0.2801894238358327 kappa 0.2865035516969219 kappa 0.28072873536022713 kappa 0.30591158260046536 kb_relative_information_score 84.6978921846983 kb_relative_information_score 86.19703731029638 kb_relative_information_score 81.97030998958832 kb_relative_information_score 82.31538203123839 kb_relative_information_score 79.74783286083105 kb_relative_information_score 80.59363989875845 kb_relative_information_score 81.33946237277071 kb_relative_information_score 78.27491929324411 kb_relative_information_score 87.01534048587 kb_relative_information_score 87.92580996755213 mean_absolute_error 0.015507953192326323 mean_absolute_error 0.015332715179737813 mean_absolute_error 0.015484437103418205 mean_absolute_error 0.01588400024358981 mean_absolute_error 0.015749816976184806 mean_absolute_error 0.015929568772726336 mean_absolute_error 0.0158923719458498 mean_absolute_error 0.01615816240970395 mean_absolute_error 0.0154111734208222 mean_absolute_error 0.015397402526732853 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 0.35625 predictive_accuracy 0.3375 predictive_accuracy 0.31875 predictive_accuracy 0.28125 predictive_accuracy 0.3125 predictive_accuracy 0.28125 predictive_accuracy 0.2875 predictive_accuracy 0.29375 predictive_accuracy 0.2875 predictive_accuracy 0.3125 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 0.35625 [0,1,1,0,0.5,0,1,1,1,0,1,0.5,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,0.5,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,0.5,1,1,0,0,0.5,1,0,0,0,0,1,1,0,0,0,1,0.5,0,1,0,1,1,0,0,0,1,0,0.5,0,0.5,1,0,0.5,0,0,0,0,1,0,0.5,0,0.5,0,0,1,0,1,0,0.5,0,0,0,0,0] recall 0.3375 [1,1,0.5,1,0,0,0.5,1,0,1,0,1,0.5,0,0,0,1,0,0.5,0,0,0,0,1,0,0,0.5,0,0.5,1,0.5,1,0,0,0,1,0,0,0,1,0,1,1,0,0,0,1,0,0,1,0,0.5,0,0.5,1,0,1,0,0,0,1,1,1,0,0,0,0.5,0,0,0,0,0,0,1,0,0.5,0,0,0.5,0.5,0,0,0,0,1,0.5,0.5,0,0,0,0.5,1,0,0,0.5,0.5,0,0,0,1] recall 0.31875 [0,0.5,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0.5,0,0,0,0,0.5,1,1,0,0,1,0,1,0.5,1,0,0,0,1,1,0.5,0,0,1,0,0,0,0,0,0.5,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5] recall 0.28125 [1,0,1,0,0,0,0,0,0,0,1,0.5,0,0,0,0,0.5,0,0,0,0,0,0,1,0,0,0.5,0,0,0.5,0,0,1,0,0,0,1,1,0,1,0,0,1,0,1,1,1,0,0,1,0,0,0,0,0,0,1,0.5,0,0,1,0,0,0,0.5,0,0.5,1,0,0,0,0.5,0,1,0,0.5,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0.5,0,0.5,0,0,0] recall 0.3125 [0,1,1,1,0,0,0,0.5,0,1,0,0.5,1,0,0,0.5,0.5,1,0,0,0,0,1,0,0,0,0.5,1,0,0.5,0,0,0,0,0,1,0.5,0,0,1,0,0.5,0,0,1,1,1,0,0,0.5,0,0,0,0,0,0,1,0,1,0,0,1,1,1,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,0,0.5,0,0,1,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0.5,1,0,0] recall 0.28125 [0,1,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,0,0.5,0,1,0,0,0.5,1,0,1,0,1,0,1,0,0,0,0.5,0,1,0,0,1,0,0,0,1,0.5,0,0.5,1,0,0,0,0,0,0,1,0,0,0.5,0,0,0,0.5,0,1,1,0,0,0,1,1,1,0,0,0.5,0,0.5,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.5] recall 0.2875 [0.5,0,0,0,0,0,0,1,0,0,1,1,0.5,0,0,0,0,0,0,0,0.5,0.5,0,1,0.5,0,0,0.5,0,1,0.5,0,1,0,1,0.5,0,1,0,1,0,0,0.5,0,0,0,0.5,0,0,1,1,0,0.5,0,0,0,0,0.5,0.5,0,0,0,0,0,0.5,1,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,1,1,0,0,0,0,1,1,0.5,0,0,0.5,0,0,0,0] recall 0.29375 [0,1,1,0,1,0,0.5,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0.5,0,0,0,1,1,0,0,1,1,0,0,1,0,0.5,1,0,0,0,1,1,0,1,0,0,0.5,1,0,0,0,0.5,0,0,0,0,1,0,1,0,1,0.5,0,0,1,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,0,0,0,0,1,1,0,0,0,1,0,0,0,0] recall 0.2875 [0,1,1,0,0,1,0.5,1,0,0,0,1,1,0,0,0,1,0,0,0,0.5,0.5,1,1,0.5,0,0,1,0,1,0,0,0,0,0.5,0,0.5,0,0,1,0,0,0.5,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0,0,0,0.5,0,0.5,0,1,0,0,0,0,0,0,1,0.5,0,0,0.5,1,0,1,0,0,0,0,0,0.5,1,0,0,0,0,1,0.5,0.5,0,0,0.5,0,0,0,0] recall 0.3125 [0,0.5,0,0,0,1,0,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0.5,1,0,0.5,1,0,0.5,0.5,0.5,1,0,1,0,0,0.5,0,0,0,1,0,1,0,0.5,0,1,0,0.5,0,0,1,0,0,0,0,0,0,1,0,0.5,1,0.5,0,0,1,0,1,0,1,1,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,0,1,1,0,0,0] relative_absolute_error 0.7832299592083989 relative_absolute_error 0.7743795545322115 relative_absolute_error 0.782042277950413 relative_absolute_error 0.8022222345247365 relative_absolute_error 0.7954453018275142 relative_absolute_error 0.8045236753902176 relative_absolute_error 0.8026450477701905 relative_absolute_error 0.8160688085709054 relative_absolute_error 0.7783420919607158 relative_absolute_error 0.7776465922592337 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 0.09050003370210022 root_mean_squared_error 0.09017079055110913 root_mean_squared_error 0.09186108548972946 root_mean_squared_error 0.09249892615250854 root_mean_squared_error 0.09280845627258533 root_mean_squared_error 0.0925656741297368 root_mean_squared_error 0.0931096767535138 root_mean_squared_error 0.0930779516028028 root_mean_squared_error 0.09108299921986655 root_mean_squared_error 0.09024916144004592 root_relative_squared_error 0.909559561528438 root_relative_squared_error 0.9062505433568268 root_relative_squared_error 0.9232386466793734 root_relative_squared_error 0.9296491865414032 root_relative_squared_error 0.9327600812977921 root_relative_squared_error 0.9303200289534679 root_relative_squared_error 0.935787461038429 root_relative_squared_error 0.9354686112768354 root_relative_squared_error 0.9154185854319113 root_relative_squared_error 0.9070382004267963 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 128.79759399947943 usercpu_time_millis 122.18872799894598 usercpu_time_millis 119.95413499971619 usercpu_time_millis 112.96703799962415 usercpu_time_millis 98.74680500070099 usercpu_time_millis 97.4703279989626 usercpu_time_millis 94.68939100042917 usercpu_time_millis 95.01043700038281 usercpu_time_millis 95.5718130007881 usercpu_time_millis 95.01487600027758 usercpu_time_millis_testing 2.016886999626877 usercpu_time_millis_testing 1.9937819997721817 usercpu_time_millis_testing 1.968450000276789 usercpu_time_millis_testing 1.684103999650688 usercpu_time_millis_testing 1.6658100012136856 usercpu_time_millis_testing 1.7177009995066328 usercpu_time_millis_testing 1.625992999834125 usercpu_time_millis_testing 1.651558999583358 usercpu_time_millis_testing 1.625859000341734 usercpu_time_millis_testing 1.6454350006824825 usercpu_time_millis_training 126.78070699985255 usercpu_time_millis_training 120.1949459991738 usercpu_time_millis_training 117.9856849994394 usercpu_time_millis_training 111.28293399997347 usercpu_time_millis_training 97.0809949994873 usercpu_time_millis_training 95.75262699945597 usercpu_time_millis_training 93.06339800059504 usercpu_time_millis_training 93.35887800079945 usercpu_time_millis_training 93.94595400044636 usercpu_time_millis_training 93.3694409995951