10110655 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) 8035922 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 4 8783 min_samples_split 6 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 63883 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 21141848 description https://api.openml.org/data/download/21141848/description.xml -1 21141849 predictions https://api.openml.org/data/download/21141849/predictions.arff area_under_roc_curve 0.7839188762626261 [0.743549,0.871508,0.874487,0.81104,0.903922,0.746922,0.746212,0.808396,0.777758,0.841935,0.8721,0.777107,0.808081,0.71224,0.872396,0.709734,0.905125,0.648299,0.710938,0.708787,0.777206,0.839844,0.777403,0.999961,0.777521,0.611644,0.679569,0.77762,0.842113,0.873047,0.84083,0.90479,0.777068,0.614978,0.871035,0.871113,0.740609,0.680339,0.904356,0.935389,0.776199,0.808337,0.873974,0.777462,0.809679,0.71297,0.935152,0.715002,0.71149,0.841126,0.838048,0.680319,0.839646,0.736072,0.714015,0.580749,0.968079,0.903212,0.742543,0.741339,0.748086,0.773635,0.746252,0.620995,0.810271,0.777107,0.615215,0.935665,0.582071,0.585878,0.677498,0.902857,0.712989,0.871745,0.578697,0.935054,0.841619,0.610953,0.842902,0.84087,0.83941,0.64968,0.71222,0.775075,0.774207,0.810646,0.900588,0.774384,0.680319,0.745936,0.935054,0.93604,0.838463,0.740037,0.841974,0.841422,0.775055,0.774069,0.670632,0.713936] average_cost 0 f_measure 0.46890828226856845 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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] precision 0.48197446263298344 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[0.487421,1,1,1,0.996835,0.996855,0.490506,0.977987,0.745253,1,0.496855,1,0.996835,0.748418,0.996835,0.981132,0.996855,0.987421,1,1,0.748418,0.990506,0.996855,1,0.75,0.484277,0.493711,0.746835,0.746835,1,1,1,0.487421,0.993711,0.993671,1,0.745253,0.743671,1,0.5,0.496855,0.748418,0.75,0.493671,0.5,0.496835,1,0.487342,0.496855,1,0.746835,0.742089,1,0.732595,0.746835,0.493711,1,0.993711,0.496835,0.742089,0.745253,0.998418,0.487342,0.496835,1,1,0.996835,0.5,0.735759,0.996855,1,0.990566,0.743671,1,0.481013,0.737342,0.996855,0.493711,1,1,1,0.996855,0.496855,0.990566,1,1,0.971519,0.743671,0.493711,0.996835,1,0.75,1,0.742089,0.993711,0.745253,0.487421,0.995253,0.737342,0.996855] area_under_roc_curve 0.820479932927315 [0.487421,0.496835,0.5,0.746835,1,0.996855,0.743671,0.493711,0.740506,1,1,0.496855,1,0.75,0.996835,1,1,1,1,1,0.996835,0.996835,0.496855,1,1,0.474843,0.987421,0.745253,0.490506,0.990566,0.5,0.75,1,0.968553,0.995253,0.740506,0.490506,0.740506,1,1,0.981132,0.990506,0.748418,0.746835,1,0.748418,1,0.490506,0.996855,0.496855,0.748418,0.745253,0.996835,0.487342,0.748418,0.490566,1,1,0.5,1,1,0.742089,0.493671,0.75,0.996855,0.5,0.732595,0.996855,0.740506,0.487421,0.481132,0.996855,0.496835,0.998418,0.734177,0.998418,1,0.490566,0.496855,0.998418,0.992089,0.490566,1,0.493711,0.998418,1,0.998418,0.990506,0.481132,0.995253,1,0.998418,1,1,1,1,1,0.743671,0.731013,0.496855] 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 0.4759273875295975 kappa 0.4191002367797948 kappa 0.4442032132001737 kappa 0.4759687475337385 kappa 0.4378429592199281 kappa 0.4251421351863551 kappa 0.4820371101460718 kappa 0.46961325966850825 kappa 0.513677811550152 kappa 0.5011248371946165 kb_relative_information_score 82.62365876536992 kb_relative_information_score 78.75628507602167 kb_relative_information_score 79.57868644772671 kb_relative_information_score 87.00126061527837 kb_relative_information_score 75.9612370215662 kb_relative_information_score 80.06428941032726 kb_relative_information_score 87.75459977923016 kb_relative_information_score 83.81308701773129 kb_relative_information_score 94.65470535967786 kb_relative_information_score 95.05694187133095 mean_absolute_error 0.011060714285714288 mean_absolute_error 0.011550595238095243 mean_absolute_error 0.01157291666666667 mean_absolute_error 0.010910714285714286 mean_absolute_error 0.011833630952380955 mean_absolute_error 0.012098511904761907 mean_absolute_error 0.010983035714285716 mean_absolute_error 0.011307142857142861 mean_absolute_error 0.010117559523809525 mean_absolute_error 0.01010089285714286 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 0.48125 predictive_accuracy 0.425 predictive_accuracy 0.45 predictive_accuracy 0.48125 predictive_accuracy 0.44375 predictive_accuracy 0.43125 predictive_accuracy 0.4875 predictive_accuracy 0.475 predictive_accuracy 0.51875 predictive_accuracy 0.50625 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 0.48125 [1,0.5,1,0,1,1,0,1,0.5,1,0.5,0.5,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,0,0,1,1,1,0.5,1,1,0,0.5,0.5,0,1,0.5,1,0,0.5,1,1,0.5,0,1,0.5,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,1,0.5,0.5,0.5,0.5,1,0,0,1,0.5,0.5,1,0.5,0.5,0,0.5,1,1,0,0.5,0,1,0,0,1] recall 0.425 [1,1,0.5,1,1,1,0,1,0.5,1,0,0.5,0,0.5,1,1,1,0,0,1,1,0,1,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0.5,1,0,0,1,0.5,0,0,1,0.5,0.5,0.5,1,1,0,1,0,0,1,0,1,0,1,0,0,0,1,1,0.5,0,1,0,0,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0,0.5,0.5,0,0,0,0.5,0,0.5,0,0,0,0.5,1,1,0,0.5,0.5,1,0,0,0] recall 0.45 [0,0.5,0.5,1,0.5,0,0,1,1,0.5,0.5,0.5,1,0.5,0,0,1,0,0,0,0,1,0,1,0,0.5,0.5,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,0,0.5,1,0.5,0,0,0,0,0.5,0,0.5,0.5,0,1,0,0,0.5,1,0,0.5,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5] recall 0.48125 [0.5,1,1,1,0.5,0,1,0,0,0.5,0.5,0.5,0,0,1,0.5,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,1,0.5,0.5,1,1,0.5,0,1,0,1,0,1,1,0.5,1,1,0,1,1,0.5,1,1,0.5,1,1,0,0,1,0.5,1,1,1,0.5,0,0,0.5,0,0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0.5,0,1,0.5,0,0,1,0,1,0,1,0,0,1,0.5,0,0,0,1,0,0,1,0,0] recall 0.44375 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,1,0.5,1,0,0,0,0.5,0.5,1,1,0,0.5,1,1,0.5,0,0,1,1,0.5,0,0,0,0,1,1,0.5,0,0.5,1,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0,0,1,0.5,0.5,0,0.5,0.5,1,0,0.5,0,0,1,0,0,0.5,0.5,1,0,0.5,1,0.5,0,0,0,0,1,0,0,0,1,0,1,1,0.5,1,1,0,1,0.5,1,0.5,1,0,0] recall 0.43125 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,0.5,0,1,1,0.5,1,0.5,0,0,0,0.5,0.5,1,0,0.5,0.5,1,1,1,1,1,0.5,0,0,0,0,0.5,0.5,0,1,0.5,1,1,0,1,1,0,0,0.5,1,1,0,0,0,0,1,1,0,0,0.5,0,0,0,0.5,0.5,0,1,0,0.5,0,0.5,0,1,0,0,0.5,0,0.5,0,1,0,0.5,0,0,1,0,0,0,0,1,1,0,0,0.5,0,0.5,0,0,0.5] recall 0.4875 [0.5,0,1,0,1,1,1,1,0,0.5,1,1,0.5,0,0,0,0,0,0,0.5,0.5,1,1,1,0,0,0,0.5,0,1,1,0,1,0,1,0.5,1,0.5,0.5,0,0.5,1,0.5,0,1,0.5,0,0.5,0,0,1,0.5,0.5,0,0,0.5,1,0.5,0,0,1,0,1,0,0.5,1,0,1,0.5,0,1,1,0,0,0,1,1,1,1,1,0.5,1,0.5,0.5,1,0,0,0,0,0,1,1,0.5,0,0,0.5,0.5,0,0.5,0.5] recall 0.475 [1,1,1,0,1,0,1,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0.5,0.5,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0,0,0.5,1,1,0.5,1,0,1,0.5,1,1,0,0,1,0,0,1,0.5,0,1,1,0.5,1,0,0.5,1,0.5,0.5,1,0,0.5,0,0,0,0.5,1,0,0.5,1,1,0,1,0,0.5,0,0,0.5,0,0.5,1,0,1,0.5,0,1,0,0,0,1,0.5,0,0,0.5] recall 0.51875 [0,1,1,1,1,1,0,0,0.5,1,0,1,1,0.5,1,0,1,0,1,1,0.5,0.5,1,1,0.5,0,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0,0,0,0,1,0,0,1,0,0.5,1,0,0,0,1,0,0,0,0.5,1,0,0,1,1,0,0,0.5,1,1,0,0.5,1,0,0,1,0,1,0.5,1,1,0,1,0.5,1,1,0.5,0,1,1,0,0.5,0.5,1,0.5,0,1,0,1] recall 0.50625 [0,0,0,0,1,1,0,0,0.5,1,1,0,1,0.5,1,1,1,1,0.5,1,0.5,1,0,1,1,0,1,0.5,0,1,0,0.5,0,0,0.5,0,0,0.5,1,1,0,0.5,0.5,0.5,1,0.5,0,0,1,0,0.5,0.5,1,0,0.5,0,1,1,0,1,1,0,0,0.5,1,0,0.5,1,0.5,0,0,0,0,0.5,0,1,1,0,0,1,0.5,0,1,0,1,1,0,0.5,0,0.5,1,0.5,1,1,1,1,1,0,0,0] relative_absolute_error 0.5586219336219328 relative_absolute_error 0.5833633958633951 relative_absolute_error 0.58449074074074 relative_absolute_error 0.5510461760461751 relative_absolute_error 0.5976581289081281 relative_absolute_error 0.6110359547859538 relative_absolute_error 0.5546987734487726 relative_absolute_error 0.5710678210678203 relative_absolute_error 0.510987854737854 relative_absolute_error 0.5101461038961032 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 0.09334422008906755 root_mean_squared_error 0.09361147361940884 root_mean_squared_error 0.09425974043991665 root_mean_squared_error 0.0905353111978754 root_mean_squared_error 0.09565304692213171 root_mean_squared_error 0.09503995012720391 root_mean_squared_error 0.09083389502416359 root_mean_squared_error 0.0916343403111685 root_mean_squared_error 0.08620757525731733 root_mean_squared_error 0.08574356725374115 root_relative_squared_error 0.9381447102539144 root_relative_squared_error 0.9408307092964598 root_relative_squared_error 0.9473460359863418 root_relative_squared_error 0.9099141137012552 root_relative_squared_error 0.961349293015061 root_relative_squared_error 0.9551874383818943 root_relative_squared_error 0.9129149940656993 root_relative_squared_error 0.9209597718905589 root_relative_squared_error 0.8664187309540826 root_relative_squared_error 0.8617552750523125 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 209.9241460000485 usercpu_time_millis 198.32761799989385 usercpu_time_millis 164.70149300039338 usercpu_time_millis 141.02483700116863 usercpu_time_millis 140.77541200094856 usercpu_time_millis 150.5330829986633 usercpu_time_millis 145.4488979979942 usercpu_time_millis 145.59673000076145 usercpu_time_millis 142.0644949994312 usercpu_time_millis 140.76629200098978 usercpu_time_millis_testing 1.7350540001643822 usercpu_time_millis_testing 1.73444699976244 usercpu_time_millis_testing 1.2998000001971377 usercpu_time_millis_testing 1.2501000001066132 usercpu_time_millis_testing 1.2474129998736316 usercpu_time_millis_testing 1.4082489997235825 usercpu_time_millis_testing 1.2454489988158457 usercpu_time_millis_testing 1.2659800013352651 usercpu_time_millis_testing 1.260839999304153 usercpu_time_millis_testing 1.253082000403083 usercpu_time_millis_training 208.1890919998841 usercpu_time_millis_training 196.5931710001314 usercpu_time_millis_training 163.40169300019625 usercpu_time_millis_training 139.77473700106202 usercpu_time_millis_training 139.52799900107493 usercpu_time_millis_training 149.12483399893972 usercpu_time_millis_training 144.20344899917836 usercpu_time_millis_training 144.3307499994262 usercpu_time_millis_training 140.80365500012704 usercpu_time_millis_training 139.5132100005867