10111042 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) 8036313 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "most_frequent" 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 1 8783 min_samples_split 17 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 1166 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 21142621 description https://api.openml.org/data/download/21142621/description.xml -1 21142623 predictions https://api.openml.org/data/download/21142623/predictions.arff area_under_roc_curve 0.8171277225378791 [0.831992,0.870837,0.874092,0.90404,0.903685,0.866339,0.805102,0.838226,0.806088,0.840692,0.869811,0.837397,0.805556,0.766967,0.870462,0.735519,0.905382,0.765467,0.764579,0.702494,0.869535,0.867069,0.835267,1,0.774582,0.632221,0.735598,0.805516,0.838246,0.81035,0.870068,0.841304,0.774562,0.792771,0.837003,0.870324,0.797329,0.702573,0.996903,0.93529,0.80236,0.835286,0.872909,0.834103,0.86989,0.769926,0.934422,0.681621,0.701566,0.838088,0.897175,0.705374,0.930417,0.754222,0.806029,0.665503,0.999684,0.899779,0.832899,0.796875,0.74781,0.801847,0.804688,0.615294,0.839291,0.836687,0.763198,0.936435,0.633799,0.731297,0.665128,0.934343,0.772372,0.869358,0.594875,0.900982,0.871015,0.69332,0.902146,0.931818,0.867523,0.676926,0.737571,0.800821,0.8314,0.839923,0.894176,0.767736,0.735381,0.806463,0.934975,0.967152,0.829526,0.760969,0.839489,0.868924,0.86336,0.77253,0.72384,0.769018] average_cost 0 f_measure 0.4525672845477093 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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 0.46536814890812067 [0.375,0.578947,0.916667,1,0.619048,0.444444,0.466667,0.714286,0.380952,0.555556,0.555556,0.642857,0.411765,0.173913,0.6,0.153846,0.8125,0.181818,0.3,0.333333,0.529412,0.588235,0.304348,1,0.4375,0.133333,0.166667,0.5,0.75,0.625,0.5625,0.611111,0.470588,0.190476,0.384615,0.666667,0.416667,0.1875,0.7,0.75,0.411765,0.357143,0.666667,0.470588,0.6875,0.315789,0.727273,0.416667,0.157895,0.588235,0.4,0.285714,0.4,0.166667,0.533333,0.058824,0.941176,0.45,0.294118,0.266667,0.875,0.375,0.375,0.428571,0.666667,0.388889,0.125,0.8,0.2,0.157895,0.086957,0.625,0.388889,0.6,0.125,0.555556,0.642857,0.090909,0.9,0.6,0.692308,0.272727,0.428571,0.2,0.32,0.692308,0.5,0.181818,0.375,0.409091,0.631579,0.785714,0.538462,0.1875,0.583333,0.5,0.375,0.368421,0.2,0.5] predictive_accuracy 0.455 prior_entropy 6.6438561897747395 recall 0.455 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[0.477987,1,1,1,1,0.996855,0.742089,0.487421,0.737342,1,0.490566,0.996855,0.996835,0.745253,0.996835,0.487421,1,0.981132,1,1,0.998418,1,0.993711,1,0.75,0.449686,0.465409,0.743671,0.742089,1,0.996835,1,0.481132,0.990566,0.993671,1,0.738924,0.735759,1,0.5,0.487421,0.742089,0.75,0.742089,0.490506,0.748418,0.996855,0.490506,0.481132,1,0.743671,0.731013,0.996835,0.719937,0.746835,0.481132,1,0.984277,0.742089,0.72943,0.745253,0.996835,0.734177,0.481013,1,1,0.985759,0.5,0.727848,0.984277,0.984277,0.987421,0.745253,1,0.471519,0.484177,0.996855,0.481132,1,0.996835,1,0.996855,0.481132,0.993711,0.985759,1,0.962025,0.742089,0.465409,0.996835,1,0.75,1,0.471519,1,0.731013,0.949686,0.998418,0.732595,1] area_under_roc_curve 0.8451116053658148 [0.962264,0.493671,0.5,0.996835,1,0.987421,0.740506,0.481132,0.737342,1,0.996855,0.484277,0.996835,0.737342,0.996835,1,1,1,0.990506,1,1,0.996835,0.496855,1,1,0.459119,1,0.743671,0.487342,0.996855,0.743671,0.75,1,0.974843,0.992089,0.742089,0.726266,0.737342,0.996835,1,0.971698,0.987342,0.75,0.743671,1,0.738924,0.993711,0.481013,0.993711,0.490566,0.995253,0.737342,0.996835,0.724684,0.746835,0.471698,1,0.993711,0.493671,1,1,0.731013,0.487342,0.742089,0.993711,0.5,0.731013,0.996855,0.987342,0.471698,0.468553,0.987421,0.490506,0.996835,0.734177,0.996835,1,0.477987,0.493711,0.996835,1,0.490566,0.996855,0.493711,0.982595,1,0.982595,0.982595,0.455975,0.998418,1,0.996835,0.996835,0.990506,1,1,1,0.743671,1,0.996855] 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 0.4631084441988078 kappa 0.42536901097166313 kappa 0.45685639851582854 kappa 0.43161634103019536 kappa 0.4190314559734775 kappa 0.41907731165397216 kappa 0.42539168870121163 kappa 0.40003947108742843 kappa 0.5261973388083864 kappa 0.5262347506810375 kb_relative_information_score 91.46715177900822 kb_relative_information_score 82.30666409233281 kb_relative_information_score 85.59423084572208 kb_relative_information_score 85.50945181991379 kb_relative_information_score 78.64983648555882 kb_relative_information_score 84.4024127727205 kb_relative_information_score 88.34598092014005 kb_relative_information_score 83.0866100111407 kb_relative_information_score 92.0933138929825 kb_relative_information_score 95.53950665902966 mean_absolute_error 0.01186389998889999 mean_absolute_error 0.012400739364801868 mean_absolute_error 0.012141284583472089 mean_absolute_error 0.012596674506049505 mean_absolute_error 0.012650273511211016 mean_absolute_error 0.012853840603840603 mean_absolute_error 0.012370142010767018 mean_absolute_error 0.013028025620213126 mean_absolute_error 0.011291602841602843 mean_absolute_error 0.011383823815073817 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 0.46875 predictive_accuracy 0.43125 predictive_accuracy 0.4625 predictive_accuracy 0.4375 predictive_accuracy 0.425 predictive_accuracy 0.425 predictive_accuracy 0.43125 predictive_accuracy 0.40625 predictive_accuracy 0.53125 predictive_accuracy 0.53125 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 0.46875 [1,0.5,1,1,1,1,0.5,1,0.5,1,1,0.5,0,0,1,0,1,0,0,0,1,0,1,1,0,0,0,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0,0,1,0.5,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,1,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,0.5,1,1,0,0,0.5,0,0,0,1] recall 0.43125 [1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,0.5,0,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0.5,1,0,0,1,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0,1,0.5,1,0,1,0,0,0,1,1,0.5,0,1,0,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0.5,0,0,0,0.5,1,0.5,0,0.5,0.5,1,0,0,0] recall 0.4625 [0,0.5,0,1,0.5,0,0,1,1,0.5,0.5,0.5,1,0.5,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,1,1,0.5,0,0,1,1,0,1,1,0,1,1,1,0,0,0.5,0,0.5,0.5,1,1,0,0,1,0.5,1,0.5,1,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0.5,1,0,0.5,0,0,1,0,0,1,0,0,1,0.5,1,0,1,0.5,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5] recall 0.4375 [0.5,1,1,0,0.5,0,1,0.5,0,0.5,0,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0.5,0.5,1,0,0.5,0.5,1,0,1,0,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,0,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0.5,0,1,0.5,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0] recall 0.425 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,1,0.5,1,0,1,0,0.5,0.5,1,1,0,0.5,1,1,0.5,0,0,1,1,0.5,0,0,0,0,0,1,0.5,0,0.5,1,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,0,1,1,0,1,1,0,1,0.5,1,0.5,1,0,0] recall 0.425 [0,1,1,1,1,0,0,0.5,1,0.5,1,1,0,1,1,0,1,0.5,0,0,0.5,0.5,0,1,0.5,0,0,1,1,0,1,1,0.5,0,0,0,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0,0,0.5,1,1,0,0,0,0,1,1,1,1,0.5,0,0,0,0.5,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,0.5,0,0.5,0,0,0.5] recall 0.43125 [0.5,0,1,0,1,0,1,1,1,0,1,1,0.5,0,0,0,0,0,0,0,0.5,1,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,1,0,0,1,0.5,0,1,0.5,0,0.5,0,1,1,0,0.5,0,0.5,0,1,0.5,0,0,1,0,1,0,0.5,1,0,1,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,0.5,0,0,0,0,1,1,0.5,0,0,0.5,0.5,0,0.5,0.5] recall 0.40625 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0.5,0.5,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,0,0,0,0,1,0.5,0,1,0.5,0.5,1,0,0.5,1,0.5,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0] recall 0.53125 [0,1,1,1,1,1,0.5,0,0.5,1,0,1,1,0.5,1,0,1,0,1,1,1,1,1,1,0.5,0,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0.5,0.5,0,0.5,1,0,0,1,0.5,0,1,0,0,0,1,0,0,0,0,1,0.5,0,1,1,0.5,0,0.5,1,1,0,0.5,1,0,0,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,1,1,0,0.5,0,1,0.5,0,1,0,1] recall 0.53125 [0,0,0,0.5,1,0,0,0,0.5,1,1,0,1,0,1,1,1,1,0.5,1,1,1,0,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,1,1,0,0.5,0.5,0.5,1,0.5,0,0,1,0,1,0,1,0,0.5,0,1,1,0,1,1,0,0,0.5,1,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0,1,1,0,1,0,1,1,0,0,0,1,1,1,1,0.5,0,1,1,0.5,0,0] relative_absolute_error 0.5991868681262611 relative_absolute_error 0.6262999679192852 relative_absolute_error 0.613196191084448 relative_absolute_error 0.6361956821237114 relative_absolute_error 0.6389027025864139 relative_absolute_error 0.6491838688808375 relative_absolute_error 0.6247546470084342 relative_absolute_error 0.6579810919299548 relative_absolute_error 0.5702829717981225 relative_absolute_error 0.5749405967208989 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 0.08718611024754971 root_mean_squared_error 0.09086591288798251 root_mean_squared_error 0.09147232997624209 root_mean_squared_error 0.08900833861965611 root_mean_squared_error 0.09279018299671389 root_mean_squared_error 0.09081222333125931 root_mean_squared_error 0.08981021986451022 root_mean_squared_error 0.09184895841459052 root_mean_squared_error 0.08523815469010826 root_mean_squared_error 0.08442548241771673 root_relative_squared_error 0.876253377641461 root_relative_squared_error 0.9132367857047177 root_relative_squared_error 0.9193315067599204 root_relative_squared_error 0.8945674618615126 root_relative_squared_error 0.9325764279651976 root_relative_squared_error 0.9126971853568044 root_relative_squared_error 0.9026266716057678 root_relative_squared_error 0.9231167649883423 root_relative_squared_error 0.8566756876647311 root_relative_squared_error 0.8485080240130699 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 291.38537000017095 usercpu_time_millis 283.2650999998805 usercpu_time_millis 218.9349400005085 usercpu_time_millis 224.33573500075 usercpu_time_millis 220.3425759998936 usercpu_time_millis 215.8680500006085 usercpu_time_millis 217.5858540003901 usercpu_time_millis 220.10264900018228 usercpu_time_millis 220.98229400126002 usercpu_time_millis 223.21117399951618 usercpu_time_millis_testing 1.7861409996839939 usercpu_time_millis_testing 1.7806850000852137 usercpu_time_millis_testing 1.6886590001377044 usercpu_time_millis_testing 1.4171080001688097 usercpu_time_millis_testing 1.4942690004318138 usercpu_time_millis_testing 1.4093480003793957 usercpu_time_millis_testing 1.4049210003577173 usercpu_time_millis_testing 1.4014210000823368 usercpu_time_millis_testing 1.3933770005678525 usercpu_time_millis_testing 1.4011619996381341 usercpu_time_millis_training 289.59922900048696 usercpu_time_millis_training 281.48441499979526 usercpu_time_millis_training 217.2462810003708 usercpu_time_millis_training 222.91862700058118 usercpu_time_millis_training 218.84830699946178 usercpu_time_millis_training 214.4587020002291 usercpu_time_millis_training 216.18093300003238 usercpu_time_millis_training 218.70122800009995 usercpu_time_millis_training 219.58891700069216 usercpu_time_millis_training 221.81001199987804