4980581 1 Jan van Rijn 9954 Supervised Classification 6970 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1) 3015978 class_weight null 6941 criterion "gini" 6941 max_depth 4 6941 max_features null 6941 max_leaf_nodes null 6941 min_impurity_split 1e-07 6941 min_samples_leaf 1 6941 min_samples_split 2 6941 min_weight_fraction_leaf 0.0 6941 presort false 6941 random_state 21866 6941 splitter "best" 6941 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "median" 6947 strategy_nominal "most_frequent" 6947 verbose 0 6947 categorical_features [] 6948 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 6948 handle_unknown "ignore" 6948 n_values "auto" 6948 sparse false 6948 threshold 0.0 6949 algorithm "SAMME.R" 6971 learning_rate 0.8198598193792997 6971 n_estimators 82 6971 random_state 14443 6971 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 10895049 description https://api.openml.org/data/download/10895049/description.xml -1 10895050 predictions https://api.openml.org/data/download/10895050/predictions.arff area_under_roc_curve 0.9059055397727275 [0.771327,0.983033,0.975024,1,0.914891,0.96295,0.949179,0.979285,0.98402,1,0.991004,0.963857,0.942985,0.887251,0.974235,0.899641,0.946575,0.929944,0.913786,0.917949,0.941091,0.90988,0.813269,0.995975,0.932213,0.89755,0.857955,0.806838,0.863518,0.945983,0.917594,0.972972,0.916134,0.924775,0.749882,0.959557,0.925308,0.919113,0.991714,0.936553,0.827099,0.792337,0.942294,0.880465,0.982797,0.883779,0.997317,0.959359,0.822857,0.812046,0.914536,0.885338,0.933416,0.86847,0.796875,0.908953,1,0.832268,0.970407,0.867049,0.883542,0.879577,0.932884,0.838009,0.9215,0.900233,0.822305,0.991635,0.69693,0.854186,0.80522,0.994792,0.873599,0.891631,0.823035,0.874428,0.876795,0.779257,0.9942,0.997672,0.868509,0.988755,0.766434,0.988676,0.856574,0.827099,0.997633,0.879991,0.873007,0.935705,0.930536,0.996567,0.847656,0.964765,0.864761,0.94474,0.894472,0.974235,0.775036,0.841501] average_cost 0 kappa 0.2342171717171717 kb_relative_information_score 539.1805031295829 mean_absolute_error 0.01818768061811349 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 0.241875 prior_entropy 6.6438561897747395 recall 0.241875 [0,0.1875,0.625,0.5625,0.3125,0.3125,0.25,0.5,0.375,0.6875,0.4375,0.5625,0.125,0.375,0.625,0.25,0.25,0,0.0625,0,0,0.25,0.125,0.75,0.3125,0,0.0625,0.0625,0.0625,0.3125,0.375,0.5625,0.125,0,0.0625,0.0625,0,0,0.3125,0.375,0.125,0.125,0.4375,0.0625,0.25,0.125,0.3125,0.25,0.25,0,0.125,0.125,0.125,0.0625,0.0625,0,0.9375,0.125,0.75,0,0,0.1875,0.5625,0.125,0.25,0.125,0.0625,0.625,0.0625,0,0.25,0.3125,0.1875,0.375,0.1875,0.125,0.125,0.0625,0.8125,0.625,0.1875,0.4375,0,0.4375,0.0625,0.25,0.6875,0.0625,0.25,0.1875,0.0625,0.375,0.0625,0.1875,0.25,0.3125,0.125,0.375,0.125,0.125] relative_absolute_error 0.9185697281875482 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.094506250133254 root_relative_squared_error 0.9498235516226613 total_cost 0 area_under_roc_curve 0.9181978196401556 [0.871069,0.977848,1,1,0.933544,1,1,1,1,1,0.984177,0.905063,0.987342,0.727848,1,0.701258,0.993671,0.974684,0.936709,0.893082,0.968553,1,0.528302,1,1,0.893987,0.935127,0.911392,0.742089,0.886792,0.879747,0.962025,0.962025,0.908228,0.901899,0.974843,0.974843,0.908805,1,0.971519,0.924528,0.943396,1,0.838608,1,0.813291,1,0.993711,0.905063,0.528481,0.985759,0.996835,0.993711,0.898734,0.748428,0.924528,1,0.937107,0.974843,0.761076,0.937107,0.754717,1,0.943038,1,0.946203,0.968354,1,0.896226,0.940252,0.768987,0.981132,0.927215,0.939873,0.974843,0.931962,0.871835,0.759494,1,1,0.897152,1,0.874214,1,0.911392,0.821203,0.993671,0.892405,0.719937,0.496855,0.950949,1,0.946203,0.974684,0.971519,0.946203,0.968553,0.971519,0.705696,0.987421] area_under_roc_curve 0.9322018549478541 [0.877358,1,0.987342,1,0.998418,1,1,0.993711,0.996835,1,0.984177,0.990506,0.933544,0.905063,1,0.974843,0.914557,0.985759,0.897152,0.959119,0.977987,0.924528,0.77044,1,0.937107,0.882911,0.938291,0.81962,0.835443,0.987421,0.884494,0.893987,1,0.924051,0.658228,0.987421,0.871069,0.940252,0.987421,0.976266,0.946541,0.95283,0.917722,0.905063,0.996835,0.922468,1,0.974843,0.952532,0.892405,0.947785,0.670886,0.996855,0.947785,0.927673,0.987421,1,0.880503,0.993711,0.892405,0.902516,0.993711,1,0.924051,1,0.955696,0.905063,1,0.701258,0.783019,0.988924,1,0.906646,0.957278,0.993711,0.860759,0.852848,0.919304,1,1,0.931962,0.990506,0.814465,0.962264,0.928797,0.824367,1,0.860759,0.993671,0.968553,0.898734,1,0.955696,0.933544,0.966772,0.886076,0.981132,0.968354,0.745253,0.987421] area_under_roc_curve 0.938671284133429 [0.876582,0.990506,0.968354,1,0.977848,0.996835,0.981132,0.974684,0.946203,1,0.990506,0.990506,0.937107,0.996835,1,0.987342,1,0.924051,0.918239,0.914557,0.993711,0.968553,0.941456,1,0.987421,0.96519,0.911392,0.899371,0.889241,1,1,1,0.901899,0.968354,0.95283,0.993711,0.943396,0.783019,0.981132,0.935127,0.890823,0.559748,1,0.880503,0.987342,0.930818,0.990506,0.974843,0.841772,0.946203,0.806962,1,0.993711,0.844937,1,0.974684,1,0.901899,0.91195,0.933544,0.921384,0.993711,1,0.767296,0.977848,0.848101,0.867089,1,0.691824,0.89557,0.762658,0.990506,0.871069,0.952532,0.899371,0.917722,0.860759,0.878165,0.990506,1,0.993711,0.996835,0.938291,1,0.811321,1,1,0.927215,0.939873,0.981132,0.96519,1,0.993711,0.987342,0.927215,0.974843,0.889241,0.971519,0.899371,0.748418] area_under_roc_curve 0.8923092807101343 [0.841772,1,1,1,0.974684,0.981013,0.974843,0.984177,0.987342,1,0.996835,1,0.90566,0.996835,0.993671,0.990506,0.838608,0.914557,0.849057,0.829114,0.90566,1,0.901899,1,0.91195,0.914557,0.781646,0.86478,0.879747,0.879747,1,1,0.664557,0.914557,0.361635,0.91195,0.91195,0.811321,1,0.873418,0.648734,0.490566,0.993711,0.852201,0.987342,0.833333,1,0.993711,0.615506,0.718354,0.873418,0.845912,0.845912,0.879747,0.86478,0.914557,1,0.81962,0.974843,0.879747,0.641509,0.194969,0.990506,0.852201,0.879747,0.756329,0.901899,0.93038,0.676101,0.925633,0.882911,0.996835,0.880503,0.829114,0.779874,0.873418,0.873418,0.786392,1,0.993671,0.877358,0.977848,0.77057,0.987342,0.710692,0.962264,1,0.879747,0.935127,0.924528,0.914557,1,0.981132,0.984177,0.852848,0.974843,0.939873,1,0.861635,0.835443] area_under_roc_curve 0.9124921632433726 [0.816456,0.937107,0.993671,1,1,0.981013,0.993711,1,1,1,0.981013,0.974684,0.987421,0.396226,1,0.920886,0.800633,0.832278,0.981132,0.901899,0.988924,0.996835,0.943038,0.987342,0.908228,0.908228,0.906646,0.845912,0.902516,0.93038,0.698113,1,0.838608,0.916139,0.959119,0.952532,0.927215,0.781646,0.990506,0.990506,0.851266,0.914557,0.921384,0.927673,1,0.889937,1,0.993671,0.981013,0.93038,0.987421,0.855346,0.954114,0.754717,0.609177,0.800633,1,1,1,0.943396,0.981013,0.816456,0.974843,0.852201,0.947785,0.908228,0.924528,0.996835,0.681962,0.919304,0.851266,1,0.622642,0.981132,0.791139,0.993711,0.881329,0.901899,1,1,0.833333,0.996835,0.743671,1,0.981132,0.63522,1,0.908805,0.816456,0.981013,0.924528,0.981013,0.899371,0.949686,0.613924,0.90566,0.962025,0.987421,0.842767,0.924051] area_under_roc_curve 0.9144777237083034 [0.765823,0.993711,0.933544,1,1,0.901899,0.924528,0.977848,1,1,1,0.974684,0.959119,0.852201,1,0.952532,0.96519,0.914557,0.91195,0.927215,0.927215,0.818038,0.802215,1,0.990506,0.908228,0.792722,0.688679,0.915094,0.925633,1,0.933962,0.996835,0.876582,0.927673,0.952532,0.901899,0.933544,0.984177,0.93038,0.696203,0.917722,0.81761,0.915094,0.974843,1,1,0.917722,0.813291,0.892405,1,1,0.849684,0.915094,0.765823,0.867089,1,0.867089,1,0.701258,0.838608,0.981013,0.993711,0.915094,0.917722,0.93038,0.380503,1,0.84019,0.844937,0.700949,0.996835,0.981132,1,0.996835,0.908805,0.917722,0.556962,1,1,0.974843,0.984177,0.93038,0.990506,0.915094,0.955975,1,0.764151,0.892405,0.969937,0.993711,1,0.915094,0.993711,0.924051,1,0.996835,0.987421,0.893082,0.80538] area_under_roc_curve 0.9082568316614921 [0.78481,1,0.955975,1,1,0.993671,0.946203,0.987342,0.993711,1,1,1,0.89557,0.974843,1,0.841772,0.993711,0.91195,0.848101,0.96519,0.920886,0.718354,0.810127,1,0.90981,0.710692,0.899371,0.778481,0.845912,1,0.996835,1,0.981132,0.943396,0.955696,0.993671,0.898734,0.993671,0.993671,1,0.958861,0.699367,0.867089,0.867089,0.823899,0.911392,0.990506,0.939873,0.845912,0.852201,0.86478,0.810127,0.867089,0.86478,0.85443,0.93038,1,0.761076,0.974684,0.852201,0.943038,0.822785,1,0.797468,0.867089,0.968553,0.943396,0.990506,0.778481,0.867089,0.962264,1,0.998418,1,0.77057,0.86478,0.86478,0.688679,0.981132,1,0.867089,1,0.78481,0.977848,0.867089,0.726266,1,0.86478,0.845912,0.968354,1,1,0.591772,1,1,1,0.898734,0.968553,0.955696,0.889241] area_under_roc_curve 0.9241309907650663 [0.851266,0.993711,0.987421,1,0.937107,0.987342,0.96519,0.981013,1,1,0.962264,0.993711,0.993671,1,0.855346,0.987342,1,0.955975,0.962025,0.984177,0.96519,1,0.765823,1,0.981013,0.90566,0.924528,0.857595,0.880503,0.927215,0.686709,1,0.955975,0.930818,0.882911,0.971519,0.927215,0.96519,0.996835,0.996855,0.908228,0.838608,0.993671,0.958861,1,0.882911,1,0.955696,0.937107,0.880503,0.899371,0.995253,0.889241,0.786164,0.958861,0.917722,1,0.871835,0.718354,0.735849,0.851266,1,0.993711,0.882911,0.924051,0.849057,0.962264,1,0.537975,0.863924,0.754717,1,0.856013,1,0.806962,0.767296,0.987421,0.660377,1,1,0.969937,1,0.882911,0.993671,0.838608,0.968354,1,0.987421,0.698113,1,0.893082,1,0.882911,0.962264,0.977987,0.876582,0.936709,0.955975,0.920886,0.924051] area_under_roc_curve 0.9091228554653293 [0.839623,0.952532,1,1,0.848101,0.880503,0.848101,0.987421,0.943038,1,1,1,0.870253,1,1,0.987421,0.993711,0.880503,0.954114,0.918239,0.966772,0.993671,0.641509,0.993671,0.928797,0.91195,0.415094,0.810127,0.886076,1,1,1,0.974843,0.962264,0.748418,1,0.982595,0.941456,0.993671,0.971698,0.77044,0.871835,1,0.892405,0.993671,0.892405,1,0.946203,0.971698,0.726415,0.808544,0.936709,0.97943,0.803797,0.772152,0.946541,1,0.764151,1,0.892405,0.821203,0.993671,1,0.892405,0.993711,0.971698,0.623418,0.993711,0.626582,0.666667,1,0.987421,0.941456,0.767405,0.870253,0.96519,0.993711,0.937107,1,1,0.734177,0.962264,0.465409,1,0.803797,0.778481,1,0.803797,1,0.920886,0.981013,1,0.867089,0.96519,0.996855,0.90981,0.918239,0.996835,0.829114,0.943396] area_under_roc_curve 0.8992843672876362 [0.320755,0.981013,0.943396,1,0.982595,0.987421,1,0.993711,0.987342,1,1,0.993711,0.998418,0.990506,1,0.849057,0.981132,0.886792,0.882911,0.918239,0.935127,0.881329,0.981132,1,0.920886,0.90566,0.899371,0.677215,0.873418,0.86478,1,0.993671,0.676101,0.893082,0.742089,0.952532,0.914557,0.89557,0.993671,0.993711,0.789308,0.746835,0.873418,0.873418,1,0.85443,1,0.977848,1,0.871069,0.873418,0.993671,0.984177,0.873418,0.928797,0.90566,1,0.525157,0.996835,0.867089,0.981013,0.64557,0.756329,0.816456,1,0.874214,0.920886,1,0.808544,0.86478,0.676101,1,0.952532,0.65981,0.780063,0.813291,0.814465,0.676101,1,0.987342,0.873418,0.981132,1,0.968553,0.810127,0.859177,1,0.816456,0.962264,0.943038,0.914557,0.993671,0.884494,0.984177,0.58805,1,0.606918,0.946203,0.803797,0.899371] 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 0.26133449078641047 kappa 0.2552268244575937 kappa 0.25584328564108627 kappa 0.2052666850632712 kappa 0.25519744368614145 kappa 0.22348484848484848 kappa 0.2495072920772566 kappa 0.281437125748503 kappa 0.1864086089321613 kappa 0.17438058849017213 kb_relative_information_score 58.23381776838064 kb_relative_information_score 55.309642603039265 kb_relative_information_score 57.72452364404185 kb_relative_information_score 52.78172824556493 kb_relative_information_score 53.8728308155574 kb_relative_information_score 50.569048283658475 kb_relative_information_score 51.75248810673443 kb_relative_information_score 53.03442752650575 kb_relative_information_score 53.7401533916554 kb_relative_information_score 52.161842744444485 mean_absolute_error 0.01770836777471978 mean_absolute_error 0.018290825391052305 mean_absolute_error 0.01803217662642821 mean_absolute_error 0.018276445606436882 mean_absolute_error 0.018319023006855423 mean_absolute_error 0.01823990668448327 mean_absolute_error 0.018340188047096008 mean_absolute_error 0.018279561971220076 mean_absolute_error 0.018179816308079567 mean_absolute_error 0.018210494764762823 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 0.26875 predictive_accuracy 0.2625 predictive_accuracy 0.2625 predictive_accuracy 0.2125 predictive_accuracy 0.2625 predictive_accuracy 0.23125 predictive_accuracy 0.25625 predictive_accuracy 0.2875 predictive_accuracy 0.19375 predictive_accuracy 0.18125 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 0.26875 [0,0,1,1,0,0,0,1,1,1,0,0.5,0.5,0,1,0,0.5,0,0,0,0,0,0,0,1,0,0,0.5,0,0,0.5,0.5,0,0,0,0,0,0,1,0.5,0,0,1,0,1,0,0,0,0,0,0.5,0.5,0,0,0,0,1,0,1,0,0,0,0.5,0,1,0,0.5,1,1,0,0,0,0,0,0,0.5,0.5,0,1,0.5,0,0.5,0,0,0,0,0,0,0.5,0,0,1,0,0,0,0.5,0,0.5,0,1] recall 0.2625 [0,0,0.5,1,1,1,0.5,1,0.5,1,0,0.5,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0.5,0,0,0,0,0,0.5,0,0,0.5,0,0.5,0,0,0,0,0,0.5,0,1,0,0,0,0.5,1,1,0,0,1,0,0,1,1,0,0.5,1,0,0,0,1,1,0.5,0.5,0,0,0,0,1,0,0.5,0,0,1,0,0,0,0,0,0.5,0,0] recall 0.2625 [0,0.5,0,0,0,0.5,0,0,0,1,0.5,0.5,0,1,0,1,0,0,0,0,0,0,0.5,1,1,0,0,0,0,1,1,1,0.5,0,1,0,0,0,0,0,0,0,1,0,0.5,1,0,0,0.5,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0.5,0,0,0,0,0,0.5,1,0,0,0.5,0,1,1,0,0.5,0,0,0,0,0.5,0,1,0.5,0,1,0] recall 0.2125 [0,0,1,1,0,0,1,0.5,0.5,1,1,0,0,1,1,0.5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0.5,0,1,0,0,0,0.5,0,0,0,0,0,0,0,0,0,1,0.5,1,0,0,0,0.5,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,1,0,0,0.5,0,0,1,0,0,0,0,0,0,0,0.5,0,0,1,0,0] recall 0.2625 [0,0,1,0,1,0.5,0,0.5,1,1,0.5,0.5,0,0,1,0,0,0,1,0,0,0.5,0.5,0.5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,1,0,0,0,0,0.5,1,0.5,0,0,0,0,0,0,0,1,0.5,1,0,0,0,0,0,0.5,0,0,0.5,0,0,0,0.5,0,0,0,1,0,0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0.5,0,0,0.5,0,0,0,0,0] recall 0.23125 [0,1,0.5,1,0,0,0,0.5,0,0.5,0.5,0.5,0,0,0,0.5,0.5,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,1,1,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,1,0.5,0,0,0,0,0,0,0,0,1,0,1,0,0,0.5,1,0,0,1,0,0.5,0,0,0,0,0,1,0.5,0,0,0,1,1,0,0.5,0,0.5,0,0,1,1,0,0.5,0,0,0,0,0,1,0,0,0,0] recall 0.25625 [0,1,0,0.5,1,0.5,0,0.5,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0.5,0,0,1,1,1,1,0,0,0,0,0,1,0,1,0,0.5,0,0.5,1,0,0,0,1,1,0.5,1,1,0,0,0,0.5,0.5] recall 0.2875 [0,0,1,0.5,0,0.5,0,0.5,1,0,0,1,0.5,1,0,0,1,0,0,0,0,1,0,1,0.5,0,1,0,1,0.5,0,1,0,0,0,0,0,0,0.5,1,0,0,1,0,1,0,0,0,0,0,0,0.5,0.5,0,0,0,1,0,0.5,0,0,1,0,0,0.5,0,0,0.5,0,0,0,0,0,1,0,0,1,0,1,1,0,1,0,0.5,0,0.5,1,0,0,1,0,0.5,0,0,1,0,0.5,0,0,0] recall 0.19375 [0,0,1,1,0,0,0.5,0,0,0,0,1,0,0.5,0.5,0,1,0,0,0,0,0,0,0.5,0.5,0,0,0,0,1,0.5,1,1,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0.5,0,0,0,1,0.5,0,0,0,1,0,0,0.5,0,0,0,0,0,0,0.5,0,0,0,0.5,0,0] recall 0.18125 [0,0,0,0,0.5,0,0.5,1,0,0,1,1,0,0,0.5,0,0,0,0,0,0,0.5,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0.5,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0.5,0.5,0,0,0,0,1,0,0,0,0,0,0,0.5,0,0,1,0,0,0,0,0,0,1,0,0.5,0,0] relative_absolute_error 0.8943620088242298 relative_absolute_error 0.9237790601541553 relative_absolute_error 0.9107159912337464 relative_absolute_error 0.9230528084059015 relative_absolute_error 0.9252031821644137 relative_absolute_error 0.9212074083072344 relative_absolute_error 0.926272123590706 relative_absolute_error 0.9232102005666689 relative_absolute_error 0.9181725408120979 relative_absolute_error 0.9197219578163026 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 0.09261590097307276 root_mean_squared_error 0.09497905707303425 root_mean_squared_error 0.0936441065721702 root_mean_squared_error 0.09517462692472481 root_mean_squared_error 0.09510588530274859 root_mean_squared_error 0.0946987104848579 root_mean_squared_error 0.09468548155194302 root_mean_squared_error 0.09416405081713543 root_mean_squared_error 0.09503288507217666 root_mean_squared_error 0.09493010653171652 root_relative_squared_error 0.9308248277224053 root_relative_squared_error 0.954575440160623 root_relative_squared_error 0.9411586828119469 root_relative_squared_error 0.9565409911253595 root_relative_squared_error 0.9558501118296759 root_relative_squared_error 0.9517578509356617 root_relative_squared_error 0.9516248951573119 root_relative_squared_error 0.9463843190921116 root_relative_squared_error 0.9551164319072016 root_relative_squared_error 0.9540834687096057 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 1904.1111550000003 usercpu_time_millis 1706.0130739999995 usercpu_time_millis 1706.8652299999999 usercpu_time_millis 1725.3011069999982 usercpu_time_millis 1705.3888449999997 usercpu_time_millis 1702.4497220000007 usercpu_time_millis 1702.6370010000012 usercpu_time_millis 1709.3388799999952 usercpu_time_millis 1702.7670970000058 usercpu_time_millis 1739.8269990000017 usercpu_time_millis_testing 101.42379000000012 usercpu_time_millis_testing 100.66775700000008 usercpu_time_millis_testing 101.60057199999972 usercpu_time_millis_testing 101.0327379999989 usercpu_time_millis_testing 100.00064499999972 usercpu_time_millis_testing 100.65511600000043 usercpu_time_millis_testing 100.60593800000106 usercpu_time_millis_testing 108.13023899999763 usercpu_time_millis_testing 100.51085800000337 usercpu_time_millis_testing 107.05744499999881 usercpu_time_millis_training 1802.6873650000002 usercpu_time_millis_training 1605.3453169999993 usercpu_time_millis_training 1605.2646580000003 usercpu_time_millis_training 1624.2683689999992 usercpu_time_millis_training 1605.3882 usercpu_time_millis_training 1601.7946060000004 usercpu_time_millis_training 1602.0310630000001 usercpu_time_millis_training 1601.2086409999977 usercpu_time_millis_training 1602.2562390000026 usercpu_time_millis_training 1632.769554000003