8832409 1 Jan van Rijn 9954 Supervised Classification 7707 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 6807459 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "most_frequent" 7660 strategy_nominal "most_frequent" 7660 verbose 0 7660 categorical_features [] 7661 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7661 handle_unknown "ignore" 7661 n_values "auto" 7661 sparse true 7661 threshold 0.0 7662 copy true 7663 with_mean false 7663 with_std true 7663 C 0.8303865226317884 7708 cache_size 200 7708 class_weight null 7708 coef0 0.5965833577366391 7708 decision_function_shape null 7708 degree 5 7708 gamma 0.001804239661055018 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 10155 7708 shrinking false 7708 tol 0.00040225916292384165 7708 verbose false 7708 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18580582 description https://api.openml.org/data/download/18580582/description.xml -1 18580583 predictions https://api.openml.org/data/download/18580583/predictions.arff area_under_roc_curve 0.9719949494949494 [0.906645,0.996922,1,0.999803,0.998382,0.990451,0.998658,0.999408,0.993332,1,0.997751,0.999132,0.979601,0.965554,0.999684,0.961687,1,0.915956,0.949574,0.901594,0.974077,0.997988,0.974747,0.999487,0.989386,0.938565,0.909249,0.986545,0.983428,0.993766,0.998382,0.999882,0.866832,0.983112,0.975221,0.992977,0.968,0.92586,0.998382,0.999448,0.922191,0.992977,1,0.968829,0.997041,0.900568,0.997633,0.990609,0.976918,0.972696,0.93383,0.99491,0.985283,0.922506,0.990136,0.952375,1,0.99779,0.975537,0.944089,0.987374,0.878275,0.988834,0.991556,0.997435,0.977549,0.88084,0.999961,0.985638,0.966974,0.925545,0.996094,0.969973,0.997277,0.957939,0.993924,0.997396,0.888336,0.997633,0.998935,0.949653,0.990412,0.978733,0.976681,0.965593,0.9927,0.999921,0.976286,0.963936,0.987768,0.929411,0.999645,0.995462,0.967764,0.974235,0.989583,0.961056,0.990254,0.841896,0.965633] average_cost 0 kappa 0.5145202020202021 kb_relative_information_score 646.5274459708766 mean_absolute_error 0.018139661856765143 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 0.519375 prior_entropy 6.6438561897747395 recall 0.519375 [0,0.75,1,0.875,0.875,0.625,1,1,0.5,0.875,0.9375,1,0.25,0.625,0.9375,0.625,1,0,0,0,0.5625,0.9375,0.0625,1,0.6875,0,0.125,0.5625,0.375,1,0.875,0.9375,0.125,0.25,0.3125,1,0.1875,0.0625,0.9375,0.9375,0.1875,0.8125,1,0.25,0.9375,0.25,0.75,0.5625,0.3125,0.25,0.0625,0.8125,0.25,0.0625,0.1875,0,1,0.75,0.875,0.25,0.6875,0.25,0.125,0.625,0.9375,0.375,0,1,0.4375,0.375,0.0625,0.625,0.1875,0.9375,0.25,0.5625,1,0.0625,0.9375,0.9375,0.25,0.625,0.375,0.1875,0.125,0.625,0.875,0.125,0.375,0.375,0.1875,0.75,0.75,0.1875,0.6875,0.625,0.25,0.5625,0.0625,0.1875] relative_absolute_error 0.9161445382204602 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.0925794521742909 root_relative_squared_error 0.9304585035114394 total_cost 0 area_under_roc_curve 0.9752654545816417 [0.886792,1,1,1,1,1,1,1,0.993671,1,1,0.996835,0.974684,0.93038,1,0.949686,1,0.955696,0.939873,0.861635,0.962264,1,1,1,0.987421,0.927215,0.911392,0.996835,0.993671,0.981132,1,1,0.870253,0.984177,0.984177,0.993711,1,0.943396,1,1,0.987421,0.987421,1,0.981013,1,0.898734,1,1,0.987342,0.943038,0.924051,1,0.987421,0.952532,0.987421,0.981132,1,1,0.993711,0.924051,0.993711,0.968553,0.987342,0.993671,1,0.977848,0.917722,1,1,0.918239,0.901899,1,0.990506,1,1,1,1,0.882911,1,0.996835,1,0.996835,0.993711,0.993711,0.974684,0.996835,1,0.993671,0.962025,0.993711,0.927215,1,1,0.977848,0.981013,0.987342,0.949686,0.981013,0.879747,0.987421] area_under_roc_curve 0.9733649789029538 [0.949686,1,1,1,1,1,1,1,0.993671,1,0.996835,1,0.987342,0.882911,1,1,1,0.958861,0.962025,0.886792,1,1,0.962264,1,1,0.955696,0.89557,0.993671,0.987342,1,1,1,0.876582,0.987342,0.96519,0.993711,0.993711,0.91195,1,1,0.974843,0.993711,1,0.96519,1,0.892405,1,1,0.987342,0.981013,0.93038,0.990506,1,0.93038,0.993711,0.943396,1,1,0.962264,0.949367,0.993711,0.930818,0.993671,0.990506,1,0.981013,0.873418,1,0.955975,0.987421,0.924051,1,0.971519,1,1,1,0.996835,0.892405,0.993671,1,0.933544,0.987342,1,0.968553,0.987342,1,1,0.993671,0.974684,1,0.96519,1,0.996835,0.927215,0.981013,0.993671,0.955975,0.981013,0.794304,0.974843] area_under_roc_curve 0.9795338746915055 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[0.958861,1,1,1,1,1,1,1,0.987421,1,0.990506,1,0.993711,0.943396,1,0.946203,1,0.939873,0.918239,0.882911,0.984177,1,0.987342,1,0.993671,0.920886,0.901899,1,0.993711,1,1,1,0.810127,0.977848,0.981132,1,0.955696,0.892405,0.996835,1,0.958861,0.996835,1,0.993711,1,0.955975,1,1,0.996835,0.981013,0.981132,0.981132,1,1,0.996835,0.955696,1,0.996835,0.974684,0.981132,0.974684,0.835443,0.981132,1,1,1,0.943396,1,0.984177,0.990506,0.920886,1,1,1,0.835443,1,0.990506,0.911392,1,1,0.968553,0.996835,0.990506,0.96519,0.981132,0.987421,1,1,0.987342,0.990506,0.930818,1,1,0.955975,0.96519,0.993711,0.987342,1,0.823899,0.987342] area_under_roc_curve 0.9724932330228484 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0.5017344686218859 kappa 0.5142338932260864 kb_relative_information_score 64.0652247042145 kb_relative_information_score 64.41630969329955 kb_relative_information_score 64.60388485361051 kb_relative_information_score 63.598500252792526 kb_relative_information_score 64.00052788674495 kb_relative_information_score 64.73402886639599 kb_relative_information_score 65.98696747115216 kb_relative_information_score 64.57600082833721 kb_relative_information_score 64.80132568496185 kb_relative_information_score 65.74467572936828 mean_absolute_error 0.018110113658516596 mean_absolute_error 0.01813299616228941 mean_absolute_error 0.018183693374284386 mean_absolute_error 0.018294182152196622 mean_absolute_error 0.0182451297222698 mean_absolute_error 0.018122925225806694 mean_absolute_error 0.017984985375378716 mean_absolute_error 0.01822906431444115 mean_absolute_error 0.018091712334452333 mean_absolute_error 0.018001816248015813 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 0.50625 predictive_accuracy 0.5 predictive_accuracy 0.525 predictive_accuracy 0.5 predictive_accuracy 0.55 predictive_accuracy 0.53125 predictive_accuracy 0.51875 predictive_accuracy 0.5375 predictive_accuracy 0.50625 predictive_accuracy 0.51875 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 0.50625 [0,0.5,1,0,0.5,1,1,1,0,1,0.5,1,0,0,1,1,1,0,0,0,1,1,0,1,0,0,0,1,0.5,1,1,1,0,0.5,0,1,0,0,1,1,1,1,1,0,1,0,1,1,0.5,0,0,1,1,0,1,0,1,1,1,0,1,1,0,0,1,0.5,0,1,1,1,0,1,0,1,0,0.5,1,0,1,1,0,1,1,1,0,0,0.5,0,0,1,0,1,0.5,0,1,0,1,1,0,1] recall 0.5 [0,0.5,1,0,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,0,0,0,1,1,0,1,1,0,0,0.5,1,1,1,1,0,0,0.5,1,0,1,1,1,1,1,1,0,1,0,1,1,0,0,0,0.5,1,0,0,0,1,1,1,0.5,1,1,0,0.5,1,0,0,1,1,1,0,1,0,1,0,0,1,0,1,1,0,0.5,1,0,0,0,1,0,0,1,0,1,0.5,0,0.5,0.5,0,0.5,0,0] recall 0.525 [0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0,1,1,0,1,1,0,0,1,0,1,1,1,0,0.5,1,1,0,0,1,0.5,0,1,1,1,1,1,0.5,1,0,0,0,1,0,0,0,0,1,0.5,1,0,1,1,0,1,1,0.5,0,1,1,0.5,0,0.5,0,1,1,0.5,1,0,0.5,1,1,0.5,0.5,0,1,1,1,0,0,1,0,0,1,0,0.5,1,0,0.5,0,0] recall 0.5 [0,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,0.5,1,0,1,0,0,0,1,1,0,1,0,0,0,1,0,1,1,1,0,0,1,1,0,0,1,1,0,1,1,1,1,1,0.5,1,0,0,0,1,1,0,0,0,1,0.5,1,0,1,1,0,1,1,0,0,1,1,0.5,0,0.5,1,1,1,0.5,1,0,1,1,1,0,0.5,0,0,1,1,0,0,1,0,1,1,0,0.5,1,0.5,0.5,0,0] recall 0.55 [0,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0,0,0,0.5,1,0,1,1,0,0,1,1,1,1,1,0,0.5,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,0.5,1,1,0.5,0,0,1,1,0,0,1,0,0,0,1,1,1,0.5,1,1,0,1,1,1,1,0,0,1,1,1,1,0.5,0,0,0.5,1,1,0.5,1,0,1,1,0] recall 0.53125 [0,1,1,1,1,0,1,1,1,0.5,1,1,0,1,1,0.5,1,0,0,0,1,0.5,0,1,1,0,0,1,1,1,1,1,0,0,1,1,0,0,1,1,0,1,1,1,1,1,1,0,0,0,0,1,0,0,0.5,0,1,1,1,1,1,0,1,1,1,0,0,1,0,0,0,0.5,1,1,0,1,1,0,1,1,1,0.5,0,0,0,1,1,1,0.5,0,1,0,1,1,0.5,1,0,1,0,0] recall 0.51875 [0,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,0,0,0,0,1,0.5,1,0.5,0,0,0,1,1,0.5,0,1,1,0,1,0.5,0,1,1,0,0.5,1,0,0,0,0,0.5,1,1,1,1,0,0,0,0,1,0.5,1,0,0.5,0,0,0.5,1,1,0,1,1,0,1,1,0,1,0,1,1,1,1,1,0,1,0,0,0,1,1,0,1,0.5,1,1,0,1,1,0.5,0,1,0,0] recall 0.5375 [0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0,1,0,0,0,0.5,1,0,1,0.5,0,1,0,0,1,0.5,1,0,0,0,1,0.5,0,1,1,0,1,1,0,1,0,1,1,1,1,0,0.5,0,1,0,0,1,1,1,1,0.5,0,1,0.5,0.5,1,0,1,0.5,0,0,0.5,0,1,0.5,1,1,0,1,1,0,1,0,0.5,0,0.5,1,0,1,0,1,1,1,0,1,1,0,1,0,0] recall 0.50625 [0,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,0,0,0,1,0,1,0.5,0,1,0,0,1,1,1,0,0,0,1,0,0,0.5,1,0,1,1,0,1,0,1,0,1,1,0,1,0,0,0.5,0,1,1,0,0,0.5,0,0,0.5,1,1,0,1,0,1,0,0,0,1,0,1,1,0,1,0.5,0,1,1,1,0,1,1,0,1,0.5,0,1,1,0,1,0.5,1,0,0,1] recall 0.51875 [0,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,0,0,0,0.5,1,0,1,1,0,0,1,0,1,1,1,1,0,0,1,0,0,1,1,1,0.5,1,0,1,0,1,1,1,1,0,1,0.5,0,0,0,1,1,1,0,0.5,0,0,1,1,1,0,1,0,1,0,0,0,0.5,0,0,1,0,1,1,0,0,1,0,0,0.5,0.5,0,1,0,0,1,1,0,1,0.5,1,0,0,1] relative_absolute_error 0.9146522049755841 relative_absolute_error 0.915807886984312 relative_absolute_error 0.9183683522365836 relative_absolute_error 0.9239485935452825 relative_absolute_error 0.9214711980944327 relative_absolute_error 0.9152992538286194 relative_absolute_error 0.9083325947160954 relative_absolute_error 0.9206598138606625 relative_absolute_error 0.9137228451743588 relative_absolute_error 0.9091826387886759 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 0.09250974367382228 root_mean_squared_error 0.09245918846383451 root_mean_squared_error 0.0927115758339801 root_mean_squared_error 0.09309371249763965 root_mean_squared_error 0.09288085568947005 root_mean_squared_error 0.09257375007652796 root_mean_squared_error 0.09203065673624178 root_mean_squared_error 0.09272646880254362 root_mean_squared_error 0.09260286293988143 root_mean_squared_error 0.09220106976235484 root_relative_squared_error 0.9297579067212801 root_relative_squared_error 0.9292498077433193 root_relative_squared_error 0.9317863962542207 root_relative_squared_error 0.935627014229969 root_relative_squared_error 0.933487722815511 root_relative_squared_error 0.9304011952726592 root_relative_squared_error 0.9249429018306285 root_relative_squared_error 0.9319360762200987 root_relative_squared_error 0.9306937905584662 root_relative_squared_error 0.9266556171851925 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 1970.6821919990034 usercpu_time_millis 2035.119747999488 usercpu_time_millis 1995.9480720008287 usercpu_time_millis 1964.3899009988672 usercpu_time_millis 1986.2537829994835 usercpu_time_millis 1995.3051709999272 usercpu_time_millis 1994.7501180013205 usercpu_time_millis 1987.139427999864 usercpu_time_millis 1988.1742279994796 usercpu_time_millis 1992.19316700146 usercpu_time_millis_testing 157.32792500057258 usercpu_time_millis_testing 158.36207900065347 usercpu_time_millis_testing 158.14787999988766 usercpu_time_millis_testing 153.4796389987605 usercpu_time_millis_testing 159.1585579990351 usercpu_time_millis_testing 154.03565400083608 usercpu_time_millis_testing 162.71975000017846 usercpu_time_millis_testing 153.40501900027448 usercpu_time_millis_testing 151.56922999995004 usercpu_time_millis_testing 154.2719040007796 usercpu_time_millis_training 1813.3542669984308 usercpu_time_millis_training 1876.7576689988346 usercpu_time_millis_training 1837.800192000941 usercpu_time_millis_training 1810.9102620001067 usercpu_time_millis_training 1827.0952250004484 usercpu_time_millis_training 1841.2695169990911 usercpu_time_millis_training 1832.030368001142 usercpu_time_millis_training 1833.7344089995895 usercpu_time_millis_training 1836.6049979995296 usercpu_time_millis_training 1837.9212630006805