10403132 1 Jan van Rijn 9954 Supervised Classification 16360 sklearn.svm.classes.SVC(32) 8240900 C 40.17005239585696 16360 cache_size 200 16360 class_weight null 16360 coef0 -0.6583864312419172 16360 decision_function_shape "ovr" 16360 degree 3 16360 gamma 0.3899322854342229 16360 kernel "sigmoid" 16360 max_iter -1 16360 probability false 16360 random_state 54970 16360 shrinking false 16360 tol 0.0005049151226151174 16360 verbose false 16360 openml-python Sklearn_0.21.2. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21728049 description https://api.openml.org/data/download/21728049/description.xml -1 21728050 predictions https://api.openml.org/data/download/21728050/predictions.arff area_under_roc_curve 0.7449494949494951 [0.620581,0.649937,1,1,0.779672,0.683081,0.904672,0.810606,0.654356,0.90625,0.686237,0.71654,0.622475,0.530934,0.999684,0.654672,0.936869,0.52178,0.583018,0.557134,0.59375,0.90625,0.684659,1,0.686553,0.498422,0.558081,0.81029,0.810606,0.90625,0.936869,0.96875,0.685606,0.714331,0.618687,0.841225,0.619634,0.556187,0.778409,0.968434,0.745581,0.840593,0.967803,0.870581,0.84375,0.71654,0.810606,0.622475,0.682765,0.809028,0.716856,0.714962,0.622159,0.686553,0.746212,0.557765,0.999684,0.9375,0.684975,0.653093,0.904356,0.811237,0.840278,0.74779,0.904356,0.559343,0.555556,1,0.653725,0.589646,0.655619,0.780934,0.713384,0.778725,0.558081,0.812184,0.779987,0.589331,0.999369,0.905934,0.593119,0.77904,0.716225,0.683712,0.653725,0.811553,1,0.559028,0.905303,0.61774,0.59154,0.967172,0.68529,0.619318,0.870581,0.686237,0.616477,0.561237,0.498422,0.748422] average_cost 0 f_measure 0.4981878080125678 [0.235294,0.243902,1,1,0.6,0.333333,0.764706,0.625,0.37037,0.896552,0.461538,0.466667,0.285714,0.111111,0.969697,0.384615,0.875,0.042553,0.113208,0.114286,0.315789,0.896552,0.387097,1,0.48,0,0.125,0.606061,0.625,0.896552,0.875,0.967742,0.428571,0.378378,0.2,0.628571,0.216216,0.105263,0.529412,0.9375,0.421053,0.594595,0.882353,0.571429,0.814815,0.466667,0.625,0.285714,0.324324,0.540541,0.482759,0.4,0.275862,0.48,0.444444,0.121212,0.969697,0.933333,0.4,0.322581,0.742857,0.666667,0.578947,0.516129,0.742857,0.142857,0.1,1,0.344828,0.1875,0.434783,0.692308,0.35,0.545455,0.125,0.740741,0.62069,0.181818,0.941176,0.866667,0.285714,0.5625,0.451613,0.352941,0.344828,0.689655,1,0.137931,0.8125,0.186047,0.230769,0.833333,0.413793,0.210526,0.571429,0.461538,0.170213,0.181818,0,0.551724] kappa 0.4898989898989899 kb_relative_information_score 0.4938978866358879 mean_absolute_error 0.01009999999999984 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] precision 0.528745542993121 [0.222222,0.2,1,1,0.642857,0.3,0.722222,0.625,0.454545,1,0.6,0.5,0.333333,0.5,0.941176,0.5,0.875,0.032258,0.081081,0.105263,1,1,0.4,1,0.666667,0,0.125,0.588235,0.625,1,0.875,1,0.5,0.333333,0.166667,0.578947,0.190476,0.090909,0.5,0.9375,0.363636,0.52381,0.833333,0.461538,1,0.5,0.625,0.333333,0.285714,0.47619,0.538462,0.368421,0.307692,0.666667,0.4,0.117647,0.941176,1,0.428571,0.333333,0.684211,0.714286,0.5,0.533333,0.684211,0.166667,0.083333,1,0.384615,0.1875,0.714286,0.9,0.291667,0.529412,0.125,0.909091,0.692308,0.176471,0.888889,0.928571,0.6,0.5625,0.466667,0.333333,0.384615,0.769231,1,0.153846,0.8125,0.148148,0.3,0.75,0.461538,0.181818,0.461538,0.6,0.129032,0.333333,0,0.615385] predictive_accuracy 0.495 prior_entropy 6.643856189774611 recall 0.495 [0.25,0.3125,1,1,0.5625,0.375,0.8125,0.625,0.3125,0.8125,0.375,0.4375,0.25,0.0625,1,0.3125,0.875,0.0625,0.1875,0.125,0.1875,0.8125,0.375,1,0.375,0,0.125,0.625,0.625,0.8125,0.875,0.9375,0.375,0.4375,0.25,0.6875,0.25,0.125,0.5625,0.9375,0.5,0.6875,0.9375,0.75,0.6875,0.4375,0.625,0.25,0.375,0.625,0.4375,0.4375,0.25,0.375,0.5,0.125,1,0.875,0.375,0.3125,0.8125,0.625,0.6875,0.5,0.8125,0.125,0.125,1,0.3125,0.1875,0.3125,0.5625,0.4375,0.5625,0.125,0.625,0.5625,0.1875,1,0.8125,0.1875,0.5625,0.4375,0.375,0.3125,0.625,1,0.125,0.8125,0.25,0.1875,0.9375,0.375,0.25,0.75,0.375,0.25,0.125,0,0.5] relative_absolute_error 0.5101010101010011 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.1004987562112081 root_relative_squared_error 1.0100505037878067 total_cost 0 area_under_roc_curve 0.7545577581402754 [0.490566,0.5,1,1,0.996835,0.987421,0.496835,0.996855,0.5,1,0.75,0.5,0.5,0.5,1,0.996855,0.996835,0.5,0.5,0.981132,0.5,1,0.990566,1,0.496855,0.5,0.5,0.996835,0.75,0.5,1,1,0.5,0.5,0.5,0.993711,0.996855,0.987421,0.993711,1,0.990566,0.993711,0.996835,0.996835,1,0.75,1,0.993711,0.75,0.743671,1,0.5,0.990566,0.5,0.993711,0.984277,1,1,0.5,0.75,0.996855,0.993711,1,0.75,1,0.5,0.5,1,0.993711,0.487421,0.75,1,1,0.5,0.487421,0.75,0.5,0.5,1,1,0.5,0.75,1,0.993711,0.5,0.75,1,0.5,0.75,0.987421,0.5,1,0.5,0.5,0.75,0.5,0.984277,0.5,0.5,0.996855] area_under_roc_curve 0.7544781466443752 [0.990566,0.5,1,1,1,0.993711,0.75,0.993711,0.5,1,0.5,0.5,0.5,0.5,1,0.996855,1,0.5,0.5,0.981132,1,0.5,0.493711,1,1,0.5,0.5,0.75,1,1,0.996835,1,0.5,0.5,0.5,0.990566,0.993711,0.993711,0.996855,1,0.984277,0.993711,1,0.996835,0.75,0.5,0.996855,0.990566,0.496835,1,1,0.746835,0.987421,0.75,0.996855,0.487421,1,1,0.996855,0.5,0.996855,0.996855,0.996835,0.496835,0.993711,0.5,0.5,1,0.496855,0.993711,0.743671,1,1,0.496835,0.493711,0.496835,0.5,0.493671,1,0.75,0.5,0.75,0.990566,1,0.5,1,1,0.5,0.75,0.984277,0.5,1,0.5,0.5,1,0.5,0.990566,0.5,0.5,0.996855] area_under_roc_curve 0.7389140991959237 [0.5,0.5,1,1,0.5,0.996835,1,0.5,1,1,0.75,0.5,0.993711,0.5,1,0.5,0.75,0.5,0.484277,0.5,0.5,0.5,0.5,1,0.996855,0.5,0.5,0.993711,0.75,1,1,1,0.75,0.5,0.981132,0.996855,0.974843,0.481132,0.993711,0.75,0.5,0.996855,1,0.984277,1,0.996855,0.5,0.993711,0.5,1,0.5,0.996855,1,0.5,0.984277,0.5,1,1,0.990566,0.5,0.996855,0.496855,0.75,0.490566,0.746835,0.5,0.5,1,0.993711,0.5,0.5,0.996835,0.981132,1,0.987421,0.75,0.5,0.5,1,1,0.496855,0.75,0.5,0.5,0.993711,1,1,0.5,0.996835,0.981132,0.75,0.996855,1,0.5,1,1,0.5,0.5,0.496855,0.75] area_under_roc_curve 0.7325451795239231 [0.496835,0.5,1,1,0.75,0.496835,1,0.5,0.746835,0.75,0.75,0.5,0.996855,0.5,1,0.5,0.996835,0.5,0.987421,0.5,0.5,1,0.746835,1,0.5,0.5,0.5,0.990566,0.5,1,1,1,0.5,0.5,0.984277,0.993711,0.981132,0.474843,0.993711,1,1,0.984277,1,0.993711,1,0.990566,0.5,0.996855,0.5,1,0.5,0.993711,0.993711,0.5,0.996855,0.5,1,1,0.990566,0.5,0.996855,0.5,0.996835,0.5,0.746835,0.5,0.5,1,0.493711,0.5,0.5,0.75,0.487421,0.75,0.987421,1,0.75,0.5,0.996835,0.75,1,0.75,0.5,0.5,0.990566,1,1,0.5,1,0.974843,0.5,1,1,0.5,1,0.993711,0.5,0.5,0.490566,0.5] area_under_roc_curve 0.7482485470901996 [0.5,0.993711,1,1,1,0.5,0.996855,0.746835,0.496855,0.75,0.5,1,0.993711,0.496855,0.996855,0.5,1,0.5,0.959119,0.5,0.5,1,0.75,1,0.5,0.5,0.5,1,0.996855,0.75,0.996855,0.5,0.746835,0.996835,0.990566,1,0.5,0.5,0.75,1,0.75,0.5,1,1,1,0.993711,1,0.5,0.75,0.5,0.5,0.490566,0.5,0.496855,0.743671,0.5,0.996855,1,0.5,0.993711,1,1,0.990566,0.996855,0.75,0.5,0.474843,1,0.5,0.5,0.5,0.75,0.990566,0.996855,0.5,1,0.996835,0.5,1,1,0.496855,0.75,0.5,0.743671,0.993711,0.996855,1,0.996855,1,0.5,0.493711,0.746835,0.981132,0.990566,0.75,0.996855,0.5,0.990566,0.5,0.75] area_under_roc_curve 0.7419592389140991 [0.5,0.990566,1,1,0.993711,0.5,0.993711,1,1,0.75,0.5,0.75,0.990566,0.5,1,0.5,0.75,0.5,0.962264,0.5,0.5,0.75,0.75,1,0.5,0.5,0.5,0.496855,1,1,1,1,0.496835,0.743671,0.981132,0.75,0.5,0.5,0.5,1,0.75,0.5,1,0.993711,1,0.996855,1,0.5,0.5,0.5,1,0.987421,0.5,1,0.746835,0.5,1,0.75,0.5,0.996855,1,1,0.993711,0.993711,1,0.5,0.968553,1,0.5,0.5,0.5,0.5,0.987421,0.987421,0.5,1,1,0.5,1,1,0.5,0.75,0.5,0.990506,0.996855,0.996855,1,0.993711,0.996835,0.5,0.493711,0.996835,0.996855,0.987421,0.5,0.996855,0.5,0.996855,0.496855,0.5] area_under_roc_curve 0.7545776610142502 [1,0.981132,1,1,0.996855,0.5,1,1,0.993711,1,0.996855,0.990566,0.5,1,1,0.75,1,0.981132,0.5,0.5,0.5,1,0.5,1,0.5,0.490566,0.474843,0.5,0.993711,1,0.75,1,0.996855,0.987421,0.5,0.75,0.5,0.5,0.993671,1,0.746835,1,1,0.5,0.5,0.5,0.5,0.5,0.984277,0.996855,0.493711,0.75,0.5,0.996855,0.75,0.5,1,1,0.5,0.990566,0.993671,0.75,0.993711,1,1,0.490566,0.493711,1,0.75,0.5,1,0.75,0.5,1,0.5,1,1,0.493711,1,1,0.5,0.993711,0.996835,0.496835,0.5,0.75,1,0.487421,0.5,0.5,0.996855,1,0.5,0.984277,0.996855,0.5,0.5,0.5,0.5,1] area_under_roc_curve 0.7451636016240744 [0.5,0.974843,1,1,0.996855,0.5,1,0.75,0.493711,1,0.5,0.993711,0.5,0.5,1,0.5,1,0.465409,0.5,0.5,0.75,1,0.496835,1,0.5,0.493711,0.996855,0.5,0.490566,1,0.75,1,1,0.993711,0.5,0.75,0.5,0.5,1,0.996855,0.493671,1,0.993671,0.5,1,0.5,1,0.5,0.987421,0.496855,0.487421,0.5,0.5,0.996855,0.75,0.5,1,1,1,0.987421,1,1,0.996855,0.75,1,0.487421,0.993711,1,0.5,0.5,1,0.75,0.5,1,0.5,1,1,0.981132,1,1,0.5,0.996855,0.75,0.75,0.5,0.5,1,0.487421,1,0.5,0.493711,0.996835,1,0.981132,0.993711,1,0.5,0.5,0.5,0.5] area_under_roc_curve 0.7388742934479737 [0.481132,0.746835,1,1,0.5,0.990566,0.996835,0.996855,0.5,1,0.993711,0.993711,0.5,0.5,1,0.993711,1,0.484277,0.5,0.487421,0.75,1,0.996855,1,1,0.5,0.990566,1,1,1,1,1,0.993711,0.996855,0.5,1,0.5,0.5,0.5,1,0.5,0.75,0.75,0.996835,0.75,0.5,0.996855,0.5,0.993711,0.987421,0.75,0.746835,0.5,0.75,0.5,0.490566,1,0.5,0.5,0.5,0.5,0.5,0.5,0.75,1,0.993711,0.5,1,0.746835,0.990566,1,0.5,0.5,0.746835,0.5,0.75,0.996855,0.993711,0.996855,0.75,0.75,0.990566,0.993711,0.490566,0.75,0.75,1,0.5,0.996855,0.5,0.5,1,0.5,0.5,0.974843,0.5,0.974843,0.5,0.5,0.996855] area_under_roc_curve 0.7482485470901994 [0.996855,0.5,1,1,0.5,0.990566,1,0.996855,0.5,1,0.996855,1,0.5,0.5,1,0.996855,1,0.474843,0.5,0.496855,0.5,1,0.996855,1,0.996835,0.5,0.493711,1,0.75,0.5,1,1,0.996855,0.987421,0.5,0.5,0.5,0.5,0.5,1,0.990566,1,1,0.993671,0.5,1,0.987421,0.5,0.990566,0.990566,0.75,0.75,0.5,0.75,0.5,0.990566,1,1,0.746835,0.5,0.75,0.75,0.496835,0.75,0.993711,0.996855,0.5,1,0.75,0.987421,0.5,1,0.5,0.746835,0.5,0.75,0.993711,0.993711,1,0.996835,0.75,0.496855,0.993711,0.496855,0.5,0.746835,1,0.5,1,0.5,0.75,0.996835,0.5,0.5,0.990566,0.5,0.965409,0.5,0.5,0.993711] 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 0.5083323484221723 kappa 0.5082936054528978 kappa 0.47698003229490765 kappa 0.464334948205916 kappa 0.49568574918245933 kappa 0.48322041909563573 kappa 0.5082936054528978 kappa 0.4894220541307174 kappa 0.4769182290846069 kappa 0.49572548556120233 kb_relative_information_score 0.5114360786831537 kb_relative_information_score 0.5114360786831539 kb_relative_information_score 0.4801178785987405 kb_relative_information_score 0.4675905985649753 kb_relative_information_score 0.4989087986493888 kb_relative_information_score 0.4863815186156235 kb_relative_information_score 0.5114360786831537 kb_relative_information_score 0.49264515863250574 kb_relative_information_score 0.48011787859874067 kb_relative_information_score 0.49890879864938875 mean_absolute_error 0.009750000000000005 mean_absolute_error 0.009750000000000005 mean_absolute_error 0.010375000000000006 mean_absolute_error 0.010625000000000006 mean_absolute_error 0.010000000000000005 mean_absolute_error 0.010250000000000006 mean_absolute_error 0.009750000000000005 mean_absolute_error 0.010125000000000006 mean_absolute_error 0.010375000000000006 mean_absolute_error 0.010000000000000005 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 0.5125 predictive_accuracy 0.5125 predictive_accuracy 0.48125 predictive_accuracy 0.46875 predictive_accuracy 0.5 predictive_accuracy 0.4875 predictive_accuracy 0.5125 predictive_accuracy 0.49375 predictive_accuracy 0.48125 predictive_accuracy 0.5 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 prior_entropy 6.643856189774732 recall 0.5125 [0,0,1,1,1,1,0,1,0,1,0.5,0,0,0,1,1,1,0,0,1,0,1,1,1,0,0,0,1,0.5,0,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,0,1,0,1,1,1,1,0,0.5,1,1,1,0.5,1,0,0,1,1,0,0.5,1,1,0,0,0.5,0,0,1,1,0,0.5,1,1,0,0.5,1,0,0.5,1,0,1,0,0,0.5,0,1,0,0,1] recall 0.5125 [1,0,1,1,1,1,0.5,1,0,1,0,0,0,0,1,1,1,0,0,1,1,0,0,1,1,0,0,0.5,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,0,1,1,1,0,1,0,0,1,0,1,0.5,1,1,0,0,0,0,0,1,0.5,0,0.5,1,1,0,1,1,0,0.5,1,0,1,0,0,1,0,1,0,0,1] recall 0.48125 [0,0,1,1,0,1,1,0,1,1,0.5,0,1,0,1,0,0.5,0,0,0,0,0,0,1,1,0,0,1,0.5,1,1,1,0.5,0,1,1,1,0,1,0.5,0,1,1,1,1,1,0,1,0,1,0,1,1,0,1,0,1,1,1,0,1,0,0.5,0,0.5,0,0,1,1,0,0,1,1,1,1,0.5,0,0,1,1,0,0.5,0,0,1,1,1,0,1,1,0.5,1,1,0,1,1,0,0,0,0.5] recall 0.46875 [0,0,1,1,0.5,0,1,0,0.5,0.5,0.5,0,1,0,1,0,1,0,1,0,0,1,0.5,1,0,0,0,1,0,1,1,1,0,0,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,0,1,1,0,1,0,1,1,1,0,1,0,1,0,0.5,0,0,1,0,0,0,0.5,0,0.5,1,1,0.5,0,1,0.5,1,0.5,0,0,1,1,1,0,1,1,0,1,1,0,1,1,0,0,0,0] recall 0.5 [0,1,1,1,1,0,1,0.5,0,0.5,0,1,1,0,1,0,1,0,1,0,0,1,0.5,1,0,0,0,1,1,0.5,1,0,0.5,1,1,1,0,0,0.5,1,0.5,0,1,1,1,1,1,0,0.5,0,0,0,0,0,0.5,0,1,1,0,1,1,1,1,1,0.5,0,0,1,0,0,0,0.5,1,1,0,1,1,0,1,1,0,0.5,0,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,1,0,1,0,0.5] recall 0.4875 [0,1,1,1,1,0,1,1,1,0.5,0,0.5,1,0,1,0,0.5,0,1,0,0,0.5,0.5,1,0,0,0,0,1,1,1,1,0,0.5,1,0.5,0,0,0,1,0.5,0,1,1,1,1,1,0,0,0,1,1,0,1,0.5,0,1,0.5,0,1,1,1,1,1,1,0,1,1,0,0,0,0,1,1,0,1,1,0,1,1,0,0.5,0,1,1,1,1,1,1,0,0,1,1,1,0,1,0,1,0,0] recall 0.5125 [1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,0.5,1,1,0,0,0,1,0,1,0,0,0,0,1,1,0.5,1,1,1,0,0.5,0,0,1,1,0.5,1,1,0,0,0,0,0,1,1,0,0.5,0,1,0.5,0,1,1,0,1,1,0.5,1,1,1,0,0,1,0.5,0,1,0.5,0,1,0,1,1,0,1,1,0,1,1,0,0,0.5,1,0,0,0,1,1,0,1,1,0,0,0,0,1] recall 0.49375 [0,1,1,1,1,0,1,0.5,0,1,0,1,0,0,1,0,1,0,0,0,0.5,1,0,1,0,0,1,0,0,1,0.5,1,1,1,0,0.5,0,0,1,1,0,1,1,0,1,0,1,0,1,0,0,0,0,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,1,1,0,0,1,0.5,0,1,0,1,1,1,1,1,0,1,0.5,0.5,0,0,1,0,1,0,0,1,1,1,1,1,0,0,0,0] recall 0.48125 [0,0.5,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,0,0,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,0,0,1,0,0.5,0.5,1,0.5,0,1,0,1,1,0.5,0.5,0,0.5,0,0,1,0,0,0,0,0,0,0.5,1,1,0,1,0.5,1,1,0,0,0.5,0,0.5,1,1,1,0.5,0.5,1,1,0,0.5,0.5,1,0,1,0,0,1,0,0,1,0,1,0,0,1] recall 0.5 [1,0,1,1,0,1,1,1,0,1,1,1,0,0,1,1,1,0,0,0,0,1,1,1,1,0,0,1,0.5,0,1,1,1,1,0,0,0,0,0,1,1,1,1,1,0,1,1,0,1,1,0.5,0.5,0,0.5,0,1,1,1,0.5,0,0.5,0.5,0,0.5,1,1,0,1,0.5,1,0,1,0,0.5,0,0.5,1,1,1,1,0.5,0,1,0,0,0.5,1,0,1,0,0.5,1,0,0,1,0,1,0,0,1] relative_absolute_error 0.4924242424242419 relative_absolute_error 0.4924242424242419 relative_absolute_error 0.5239898989898983 relative_absolute_error 0.5366161616161611 relative_absolute_error 0.5050505050505044 relative_absolute_error 0.5176767676767671 relative_absolute_error 0.4924242424242419 relative_absolute_error 0.5113636363636358 relative_absolute_error 0.5239898989898983 relative_absolute_error 0.5050505050505044 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 0.09874208829065752 root_mean_squared_error 0.09874208829065752 root_mean_squared_error 0.10185774393731684 root_mean_squared_error 0.10307764064044155 root_mean_squared_error 0.10000000000000003 root_mean_squared_error 0.10124228365658296 root_mean_squared_error 0.09874208829065752 root_mean_squared_error 0.10062305898749056 root_mean_squared_error 0.10185774393731684 root_mean_squared_error 0.10000000000000003 root_relative_squared_error 0.992395326897746 root_relative_squared_error 0.992395326897746 root_relative_squared_error 1.0237088443399311 root_relative_squared_error 1.0359692675134347 root_relative_squared_error 1.0050378152592119 root_relative_squared_error 1.017523235780655 root_relative_squared_error 0.992395326897746 root_relative_squared_error 1.011299793694863 root_relative_squared_error 1.0237088443399311 root_relative_squared_error 1.0050378152592119 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 514.4666569999572 usercpu_time_millis 510.18862400042053 usercpu_time_millis 511.71848700005285 usercpu_time_millis 514.2396420001205 usercpu_time_millis 512.9506799999035 usercpu_time_millis 512.7693790000194 usercpu_time_millis 517.0287340001778 usercpu_time_millis 513.5843419998309 usercpu_time_millis 512.4227539995445 usercpu_time_millis 511.5790360000574 usercpu_time_millis_testing 67.66712399985408 usercpu_time_millis_testing 66.4495750002061 usercpu_time_millis_testing 66.61417499981326 usercpu_time_millis_testing 67.22555699980148 usercpu_time_millis_testing 66.64748100001816 usercpu_time_millis_testing 66.61294400009865 usercpu_time_millis_testing 67.03585400009615 usercpu_time_millis_testing 67.08545699984825 usercpu_time_millis_testing 66.58649999963018 usercpu_time_millis_testing 66.6687699999784 usercpu_time_millis_training 446.7995330001031 usercpu_time_millis_training 443.73904900021444 usercpu_time_millis_training 445.1043120002396 usercpu_time_millis_training 447.014085000319 usercpu_time_millis_training 446.30319899988535 usercpu_time_millis_training 446.15643499992075 usercpu_time_millis_training 449.99288000008164 usercpu_time_millis_training 446.4988849999827 usercpu_time_millis_training 445.83625399991433 usercpu_time_millis_training 444.910266000079 wall_clock_time_millis 514.5175457000732 wall_clock_time_millis 510.2207660675049 wall_clock_time_millis 511.75808906555176 wall_clock_time_millis 514.2722129821777 wall_clock_time_millis 512.993574142456 wall_clock_time_millis 512.8209590911865 wall_clock_time_millis 517.0786380767822 wall_clock_time_millis 513.6270523071289 wall_clock_time_millis 512.4549865722656 wall_clock_time_millis 511.6159915924072 wall_clock_time_millis_testing 67.67749786376953 wall_clock_time_millis_testing 66.45941734313965 wall_clock_time_millis_testing 66.62321090698242 wall_clock_time_millis_testing 67.23499298095703 wall_clock_time_millis_testing 66.65778160095215 wall_clock_time_millis_testing 66.6346549987793 wall_clock_time_millis_testing 67.04998016357422 wall_clock_time_millis_testing 67.09575653076172 wall_clock_time_millis_testing 66.59674644470215 wall_clock_time_millis_testing 66.67757034301758 wall_clock_time_millis_training 446.8400478363037 wall_clock_time_millis_training 443.76134872436523 wall_clock_time_millis_training 445.13487815856934 wall_clock_time_millis_training 447.0372200012207 wall_clock_time_millis_training 446.3357925415039 wall_clock_time_millis_training 446.1863040924072 wall_clock_time_millis_training 450.028657913208 wall_clock_time_millis_training 446.5312957763672 wall_clock_time_millis_training 445.8582401275635 wall_clock_time_millis_training 444.93842124938965