10192641 1 Jan van Rijn 9954 Supervised Classification 9666 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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4) 8118103 copy true 9559 fill_value -1 9559 missing_values NaN 9559 strategy "constant" 9559 verbose 0 9559 n_jobs null 9606 remainder "passthrough" 9606 sparse_threshold 0.3 9606 transformer_weights null 9606 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] 9606 memory null 9607 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 9607 axis 0 9608 copy true 9608 missing_values "NaN" 9608 strategy "median" 9608 verbose 0 9608 copy true 9609 with_mean true 9609 with_std true 9609 memory null 9610 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] 9610 categorical_features null 9611 categories null 9611 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 9611 handle_unknown "ignore" 9611 n_values null 9611 sparse true 9611 threshold 0.0 9612 memory null 9666 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}] 9666 criterion "friedman_mse" 9667 init null 9667 learning_rate 0.014923214378349928 9667 loss "deviance" 9667 max_depth 24 9667 max_features 0.8530041737394063 9667 max_leaf_nodes null 9667 min_impurity_decrease 0.8308636904094762 9667 min_impurity_split null 9667 min_samples_leaf 9 9667 min_samples_split 4 9667 min_weight_fraction_leaf 0.023417335504366033 9667 n_estimators 212 9667 n_iter_no_change 1764 9667 presort "auto" 9667 random_state 55973 9667 subsample 0.5025335828060278 9667 tol 0.021176255760676613 9667 validation_fraction 0.6474944124396005 9667 verbose 0 9667 warm_start false 9667 openml-python Sklearn_0.20.3. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21306198 description https://api.openml.org/data/download/21306198/description.xml -1 21306199 predictions https://api.openml.org/data/download/21306199/predictions.arff area_under_roc_curve 0.5523800505050506 [0.73181,0.533183,0.445747,0.315538,0.400568,0.650016,0.244831,0.514362,0.470881,0.355903,0.42152,0.366359,0.770952,0.335464,0.248856,0.730271,0.543008,0.697838,0.533302,0.67006,0.838818,0.298098,0.459951,0.873501,0.486703,0.726602,0.615017,0.814216,0.477628,0.589883,0.463542,0.334596,0.806246,0.278212,0.479206,0.292337,0.4302,0.581795,0.193379,0.270005,0.708807,0.567945,0.171283,0.724215,0.193024,0.796461,0.100142,0.280974,0.906349,0.751006,0.772668,0.395833,0.552971,0.623501,0.622199,0.52616,0.553898,0.805319,0.421441,0.679589,0.400489,0.718099,0.118371,0.455966,0.234099,0.678662,0.777245,0.169034,0.549558,0.689591,0.628314,0.077415,0.570194,0.553859,0.505287,0.466027,0.5305,0.711667,0.248895,0.991832,0.787011,0.990688,0.733862,0.947404,0.885811,0.759746,0.992148,0.721334,0.770537,0.647806,0.58724,0.064512,0.313644,0.51523,0.661301,0.538865,0.707071,0.583649,0.654317,0.856534] average_cost 0 kappa 0.041035353535353536 kb_relative_information_score 87.98191170033157 mean_absolute_error 0.019620762540871667 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 0.050625 prior_entropy 6.6438561897747395 recall 0.050625 [0,0,0,0,0,0.0625,0,0,0,0,0,0,0.0625,0,0,0,0.125,0,0,0.0625,0.125,0,0,0.0625,0,0,0,0,0,0,0.0625,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0.0625,0,0,0,0,0,0,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0.875,0,0.8125,0,0.75,0.375,0,0.6875,0,0,0,0,0,0,0,0,0,0,0,0,0.4375] relative_absolute_error 0.9909476030743248 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.10004959448071439 root_relative_squared_error 1.0055362585446723 total_cost 0 area_under_roc_curve 0.55619427195287 [0.779874,0.642405,0.363924,0.213836,0.506329,0.194969,0.186709,0.100629,0.275316,0.91195,0.493671,0.275316,0.851266,0.202532,0.21519,0.761006,0.857595,0.768987,0.389241,0.918239,0.773585,0.157233,0.459119,0.993711,0.553459,0.563291,0.642405,0.857595,0.53481,0.666667,0.291139,0.427215,0.917722,0.265823,0.471519,0.138365,0.169811,0.383648,0,0.316456,0.899371,0.761006,0.091772,0.68038,0.072785,0.848101,0.301887,0.125786,0.974684,0.889241,0.898734,0.256329,0.238994,0.572785,0.704403,0.377358,0.949367,0.943396,0.062893,0.857595,0.352201,0.459119,0.072785,0.610759,0.427673,0.636076,0.886076,0.006289,0.792453,0.924528,0.731013,0.044025,0.490506,0.648734,0.377358,0.487342,0.379747,0.579114,0.297468,0.987342,0.518987,1,0.987421,0.981132,0.756329,0.75,0.990506,0.674051,0.693038,0.893082,0.544304,0.037736,0.212025,0.386076,0.75,0.509494,0.559748,0.636076,0.655063,0.974843] area_under_roc_curve 0.5390565291378073 [0.962264,0.610759,0.332278,0.383648,0.167722,0.163522,0.167722,0.264151,0.560127,0.339623,0.598101,0.335443,0.724684,0.550633,0.101266,0.924528,0.370253,0.420886,0.462025,0.660377,0.962264,0.622642,0.572327,0.981132,0.622642,0.898734,0.648734,0.734177,0.417722,0.522013,0.398734,0.25,0.93038,0.155063,0.449367,0.201258,0.308176,0.949686,0.069182,0.234177,0.883648,0.666667,0.06962,0.617089,0.091772,0.871835,0.132075,0.037736,0.939873,0.848101,0.987342,0.278481,0.45283,0.389241,0.157233,0.150943,0.503165,0.90566,0.433962,0.742089,0.27673,0.842767,0.094937,0.553797,0.050314,0.696203,0.822785,0.062893,0.798742,0.830189,0.617089,0.012579,0.544304,0.696203,0.333333,0.170886,0.56962,0.78481,0.139241,0.987342,0.591772,0.977848,1,0.389937,0.778481,0.579114,1,0.531646,0.856013,0.566038,0.626582,0,0.496835,0.727848,0.753165,0.253165,0.779874,0.829114,0.762658,0.377358] area_under_roc_curve 0.5735087771674229 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[0.677215,0.352201,0.537975,0.598101,0.257862,0.696203,0.113208,0.670886,0.666667,0.294304,0.43038,0.341772,0.685535,0.993711,0.157233,0.731013,0.693038,0.762658,0.339623,0.661392,0.971519,0.389241,0.335443,0.971519,0.575949,0.917722,0.541139,0.924528,0.427673,0.753165,0.446541,0.339623,0.950949,0.386076,0.553459,0.332278,0.620253,0.693038,0.107595,0.46519,0.797468,0.275316,0.113208,0.779874,0.031447,0.852201,0.085443,0.189873,0.908228,0.708861,0.672956,0.194969,0.471519,0.54717,0.503165,0.332278,0,0.724684,0.306962,0.685535,0.449367,0.702532,0.069182,0.289308,0.256329,0.708861,0.710692,0.03481,0.737342,0.849684,0.522152,0.075949,0.503145,0.742138,0.401899,0.937107,0.68038,0.767405,0.433544,1,0.943396,1,0.35443,0.993671,0.933962,0.993711,0.968553,0.971698,0.743671,0.737342,0.849057,0.053797,0.075472,0.761006,0.756329,0.754717,0.857595,0.339623,0.962264,0.746835] area_under_roc_curve 0.5335074834806145 [0.974684,0.490566,0.632911,0.088608,0.081761,0.838608,0.27673,0.401899,0.264151,0.408228,0.341772,0.575949,0.81761,0.540881,0.157233,0.844937,0.348101,0.721519,0.433962,0.623418,0.556962,0.253165,0.376582,0.683544,0.310127,0.787975,0.636076,0.886792,0.540881,0.591772,0.440252,0.144654,0.85443,0.275316,0.647799,0.278481,0.427215,0.585443,0.221519,0.199367,0.837025,0.651899,0.427673,0.877358,0.09434,0.861635,0.03481,0.268987,0.938291,0.803797,0.971698,0.440252,0.598101,0.578616,0.743671,0.639241,0,0.901899,0.240506,0.345912,0.348101,0.794304,0.106918,0.314465,0.088608,0.332278,0.54717,0.031646,0.541139,0.528481,0.686709,0.110759,0.509434,0.213836,0.674051,0.641509,0.262658,0.889241,0.126582,1,0.899371,0.996835,0.648734,0.971519,1,0.823899,0.968553,0.798742,0.800633,0.620253,0.641509,0.136076,0.383648,0.18239,0.598101,0.553459,0.537975,0.245283,0.779874,0.993671] area_under_roc_curve 0.5479580895231273 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[0.754717,0.550633,0.245283,0.094937,0.544304,0.528302,0.265823,0.132075,0.373418,0.106918,0.377358,0.264151,0.936709,0.205696,0.189873,0.735849,0.18239,0.72956,0.484177,0.616352,0.987342,0.287975,0.72956,0.781646,0.43038,0.874214,0.679245,0.835443,0.392405,0.264151,0.601266,0.21519,0.955975,0.207547,0.528481,0.186709,0.161392,0.496835,0.132911,0.477987,0.666667,0.471519,0.357595,0.753165,0.082278,0.81962,0.025157,0.329114,0.974843,0.955975,0.962025,0.420886,0.648734,0.693038,0.924051,0.874214,0.857595,0.773585,0.189873,0.746835,0.436709,0.64557,0.025316,0.392405,0.125786,0.937107,0.689873,0.54717,0.572785,0.886792,0.798742,0.201258,0.617089,0.338608,0.367089,0.60443,0.836478,0.672956,0.232704,0.996835,0.914557,0.993711,0.981132,0.981132,0.984177,0.658228,0.993671,0.636076,0.842767,0.594937,0.765823,0.047468,0.325949,0.648734,0.578616,0.800633,0.578616,0.259494,0.601266,0.974843] area_under_roc_curve 0.5386646913064245 [0.496855,0.424051,0.232704,0.712025,0.420886,0.119497,0.10443,0.955975,0.427215,0.371069,0.113208,0.45283,0.458861,0.186709,0.085443,0.584906,0.314465,0.654088,0.740506,0.666667,0.759494,0.493671,0.308176,0.962025,0.658228,0.251572,0.679245,0.919304,0.427215,0.654088,0.841772,0.386076,0.955975,0.132075,0.344937,0.10443,0.493671,0.436709,0.091772,0.201258,0.622642,0.455696,0.313291,0.753165,0.158228,0.787975,0.056604,0.329114,0.981132,0.666667,0.881329,0.496835,0.707278,0.746835,0.56962,0.459119,0.613924,0.874214,0.490506,0.851266,0.446203,0.471519,0.370253,0.667722,0.27044,0.672956,0.734177,0.025157,0.329114,0.921384,0.27673,0.018868,0.598101,0.598101,0.636076,0.205696,0.081761,0.811321,0.169811,0.981013,0.802215,0.974843,0.773585,1,0.966772,0.955696,0.993671,0.765823,0.603774,0.585443,0.550633,0.056962,0.123418,0.620253,0.383648,0.541139,0.798742,0.610759,0.56962,0.968553] 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 0.04044190175577037 kappa 0.03473837782421828 kappa 0.05930631732628339 kappa 0.06595140422846323 kappa 0.05284344291408501 kappa 0.03359519955785401 kappa 0.04025256511444357 kappa 0.03998736825484546 kappa 0.021426034802509566 kappa 0.021001105321332703 kb_relative_information_score 9.710578498729122 kb_relative_information_score 7.987211475383797 kb_relative_information_score 9.877224693914567 kb_relative_information_score 10.508852260343339 kb_relative_information_score 10.6398117964142 kb_relative_information_score 6.925699366877361 kb_relative_information_score 9.258461154803927 kb_relative_information_score 8.739012196517974 kb_relative_information_score 7.834345814444383 kb_relative_information_score 6.500714442902929 mean_absolute_error 0.01957447378833311 mean_absolute_error 0.019651512724546585 mean_absolute_error 0.01959656749357195 mean_absolute_error 0.01952019997642982 mean_absolute_error 0.019571512059614136 mean_absolute_error 0.01964256784592811 mean_absolute_error 0.019625531570847284 mean_absolute_error 0.019634631392105778 mean_absolute_error 0.019661528847779396 mean_absolute_error 0.019729099709560577 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 0.05 predictive_accuracy 0.04375 predictive_accuracy 0.06875 predictive_accuracy 0.075 predictive_accuracy 0.0625 predictive_accuracy 0.04375 predictive_accuracy 0.05 predictive_accuracy 0.05 predictive_accuracy 0.03125 predictive_accuracy 0.03125 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 0.05 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,1,0,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.04375 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.5,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.06875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5] recall 0.075 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5] recall 0.0625 [0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5] recall 0.04375 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0.5,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1] recall 0.05 [0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.5] recall 0.05 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1] relative_absolute_error 0.9886097872895493 relative_absolute_error 0.9925006426538663 relative_absolute_error 0.9897256309884807 relative_absolute_error 0.9858686856782722 relative_absolute_error 0.9884602050310153 relative_absolute_error 0.9920488811074786 relative_absolute_error 0.9911884631741036 relative_absolute_error 0.9916480501063508 relative_absolute_error 0.9930065074636043 relative_absolute_error 0.9964191772505326 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 0.10012592843203662 root_mean_squared_error 0.10015785261013646 root_mean_squared_error 0.09975977253589344 root_mean_squared_error 0.09956070673638473 root_mean_squared_error 0.09989247533033185 root_mean_squared_error 0.10026213588004589 root_mean_squared_error 0.10020496025364257 root_mean_squared_error 0.1000337950584266 root_mean_squared_error 0.09990411469046048 root_mean_squared_error 0.10059046260596197 root_relative_squared_error 1.0063034436213423 root_relative_squared_error 1.0066242936834566 root_relative_squared_error 1.0026234384023023 root_relative_squared_error 1.0006227518399917 root_relative_squared_error 1.0039571516683141 root_relative_squared_error 1.0076723799810352 root_relative_squared_error 1.0070977433145705 root_relative_squared_error 1.0053774683760879 root_relative_squared_error 1.004074131639061 root_relative_squared_error 1.0109721877340943 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 22757.589312001073 usercpu_time_millis 22679.27983100526 usercpu_time_millis 22647.68039500632 usercpu_time_millis 22769.8374689935 usercpu_time_millis 22681.611562999024 usercpu_time_millis 22734.88137000095 usercpu_time_millis 22647.70326099824 usercpu_time_millis 22762.08708399645 usercpu_time_millis 22639.722829990205 usercpu_time_millis 22692.120497005817 usercpu_time_millis_testing 12.937448002048768 usercpu_time_millis_testing 12.071575001755264 usercpu_time_millis_testing 14.377072002389468 usercpu_time_millis_testing 12.312351995205972 usercpu_time_millis_testing 16.103449997899588 usercpu_time_millis_testing 12.514809001004323 usercpu_time_millis_testing 12.970176001545042 usercpu_time_millis_testing 12.463958999433089 usercpu_time_millis_testing 12.867071993241552 usercpu_time_millis_testing 12.46635600546142 usercpu_time_millis_training 22744.651863999024 usercpu_time_millis_training 22667.208256003505 usercpu_time_millis_training 22633.30332300393 usercpu_time_millis_training 22757.525116998295 usercpu_time_millis_training 22665.508113001124 usercpu_time_millis_training 22722.366560999944 usercpu_time_millis_training 22634.733084996697 usercpu_time_millis_training 22749.623124997015 usercpu_time_millis_training 22626.855757996964 usercpu_time_millis_training 22679.654141000356