8829489 1 Jan van Rijn 9956 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) 6804539 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 0.2581540964807176 7708 cache_size 200 7708 class_weight null 7708 coef0 0.22170319036687847 7708 decision_function_shape null 7708 degree 3 7708 gamma 0.04822835526322003 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 41775 7708 shrinking true 7708 tol 0.012533004565159336 7708 verbose false 7708 openml-pimp openml-python Sklearn_0.18.1. study_71 1493 one-hundred-plants-texture https://www.openml.org/data/download/1592285/phpoOxxNn -1 18574766 description https://api.openml.org/data/download/18574766/description.xml -1 18574767 predictions https://api.openml.org/data/download/18574767/predictions.arff area_under_roc_curve 0.9918161993306829 [0.998359,0.99846,0.998816,0.997078,0.954161,0.997592,0.99621,0.994078,0.994551,0.999882,0.999013,0.985668,0.9985,0.999803,0.990919,0.999013,0.993999,0.999842,0.999447,0.96419,0.999882,0.95424,0.992104,0.988748,0.938566,0.997592,0.966124,0.97335,0.999842,0.998697,0.990643,0.999882,0.948594,0.999052,0.999763,0.990603,0.998381,0.999842,0.99771,0.998816,0.999131,0.979706,0.994867,0.9771,0.984089,0.999329,0.979075,0.999842,0.964229,0.998618,0.998065,0.999171,0.996684,0.990169,0.989893,0.997315,0.999921,0.983536,0.998737,1,0.999961,0.997986,0.998776,0.999566,0.999368,0.998934,0.999329,0.99388,0.996684,0.989103,0.994473,0.983457,0.999763,0.966914,0.998658,0.999329,0.996526,0.999763,0.998539,0.992577,0.996447,0.982628,0.977219,0.996723,0.99313,0.999842,0.973073,0.998816,0.999842,0.999289,0.968178,0.995539,0.995104,1,0.995973,0.995539,0.996091,0.995578,0.999131,0.998816] average_cost 0 f_measure 0.8272776946345726 [0.787879,0.933333,0.875,0.8,0.645161,0.787879,0.740741,0.727273,0.866667,0.888889,0.909091,0.628571,0.875,0.9375,0.75,0.967742,0.648649,0.903226,0.896552,0.857143,0.969697,0.594595,0.903226,0.967742,0.454545,0.875,0.83871,0.606061,0.896552,0.9375,0.882353,0.967742,0.666667,0.903226,0.857143,0.742857,0.823529,0.969697,0.8,0.857143,0.933333,0.758621,0.666667,0.512821,0.666667,0.903226,0.578947,0.903226,0.896552,0.875,0.733333,0.933333,0.823529,0.903226,0.742857,0.823529,0.933333,0.75,0.903226,0.896552,0.896552,0.909091,0.740741,0.857143,0.882353,0.827586,0.8125,0.777778,0.8125,0.875,0.848485,0.645161,0.933333,0.903226,0.933333,0.933333,0.787879,0.967742,0.848485,0.684211,0.933333,0.6875,0.588235,0.733333,0.848485,0.896552,0.56,0.903226,0.967742,0.882353,0.933333,0.709677,0.903226,0.933333,0.866667,0.823529,0.875,0.875,0.967742,0.882353] kappa 0.8205950850079112 kb_relative_information_score 946.4467401850598 mean_absolute_error 0.015917137886533463 mean_prior_absolute_error 0.019799992711748222 number_of_instances 1599 [15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16] precision 0.8433243972167924 [0.722222,1,0.875,0.857143,0.666667,0.764706,0.909091,0.705882,0.928571,0.8,0.882353,0.578947,0.875,0.9375,0.75,1,0.571429,0.933333,1,1,0.941176,0.52381,0.933333,1,0.357143,0.875,0.866667,0.588235,1,0.9375,0.833333,1,0.6,0.933333,1,0.684211,0.777778,0.941176,0.736842,1,1,0.846154,0.647059,0.434783,0.647059,0.933333,0.5,0.933333,1,0.875,0.785714,1,0.777778,0.933333,0.684211,0.777778,1,0.625,0.933333,1,1,0.882353,0.909091,1,0.833333,0.923077,0.8125,0.7,0.8125,0.875,0.823529,0.666667,1,0.933333,1,1,0.764706,1,0.823529,0.590909,1,0.6875,0.555556,0.785714,0.823529,1,0.777778,0.933333,1,0.833333,1,0.733333,0.933333,1,0.928571,0.777778,0.875,0.875,1,0.833333] predictive_accuracy 0.8223889931207005 prior_entropy 6.6438309601245775 recall 0.8223889931207005 [0.866667,0.875,0.875,0.75,0.625,0.8125,0.625,0.75,0.8125,1,0.9375,0.6875,0.875,0.9375,0.75,0.9375,0.75,0.875,0.8125,0.75,1,0.6875,0.875,0.9375,0.625,0.875,0.8125,0.625,0.8125,0.9375,0.9375,0.9375,0.75,0.875,0.75,0.8125,0.875,1,0.875,0.75,0.875,0.6875,0.6875,0.625,0.6875,0.875,0.6875,0.875,0.8125,0.875,0.6875,0.875,0.875,0.875,0.8125,0.875,0.875,0.9375,0.875,0.8125,0.8125,0.9375,0.625,0.75,0.9375,0.75,0.8125,0.875,0.8125,0.875,0.875,0.625,0.875,0.875,0.875,0.875,0.8125,0.9375,0.875,0.8125,0.875,0.6875,0.625,0.6875,0.875,0.8125,0.4375,0.875,0.9375,0.9375,0.875,0.6875,0.875,0.875,0.8125,0.875,0.875,0.875,0.9375,0.9375] relative_absolute_error 0.8038961487641919 root_mean_prior_squared_error 0.09949872432040595 root_mean_squared_error 0.08253446007161899 root_relative_squared_error 0.8295026959927787 total_cost 0 area_under_roc_curve 0.9938699148156992 [1,1,1,0.996835,1,0.984177,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,0.797468,1,0.96519,0.968354,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,0.993671,0.993671,1,1,0.993711,1,0.993711,1,1,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.993711,1,0.971519,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,0.993671,0.993711,1,0.977848,1,1,1,0.981013,1,1,1,0.990506,0.990506,1,1,1,1] area_under_roc_curve 0.9925369198312239 [1,1,1,0.996835,0.981013,1,1,1,1,1,1,0.984177,1,1,0.993671,1,1,1,1,0.977848,1,0.933544,1,1,0.920886,0.993671,0.918239,0.987342,1,1,1,1,1,1,1,0.952532,1,1,1,1,1,1,0.962264,0.996835,1,1,0.96519,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,0.984177,1,1,1,1,1,0.924528,1,0.949367,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.996835,0.867925,1,1,1,0.977848,0.996835,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1] area_under_roc_curve 0.9960452989411672 [1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,0.996835,1,1,0.993671,0.996835,1,1,1,1,0.987421,1,1,1,1,0.993711,1,1,1,1,1,1,0.984177,1,0.936709,1,1,1,0.996835,1,1,1,0.990506,1,0.911392,1,1,0.993711,1,0.920886,1,1,1,1,1,1,1,1,1,1,0.996835,1,1] area_under_roc_curve 0.9956927692858846 [1,1,0.996835,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.955696,1,1,0.974843,1,1,0.943038,1,1,1,1,0.996835,1,1,0.987421,0.993711,1,0.974843,1,1,0.993711,0.981132,0.993671,1,1,1,1,1,1,1,0.996835,0.993671,0.949367,0.968354,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,0.977848,1,1,0.993671,1,0.984177,0.987421,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1] area_under_roc_curve 0.9948677951596208 [1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.993711,0.987421,1,0.867925,1,0.968354,1,1,1,1,0.987421,0.971519,1,0.987421,0.996835,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,0.993711,0.996835,1,0.990506,0.993671,0.984177,1,0.993671,1,1,0.993711,0.996835,1,1,1,0.971519,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,0.993671,0.993671,1,0.993711,1,1,1,0.981013,0.996835,0.981132,0.993711,1,1,0.943038,1,1,1,0.981132,1,1,1,1,1,1,1,1,0.996835] area_under_roc_curve 0.9953081462463178 [1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.742138,1,1,0.930818,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,0.981013,1,1,1,1,1,1,1,1,1,0.984177,0.993671,1,0.936709,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.981132,1,1,1,1,1,1,1,1,1,1,0.984177,0.974843,1,1,1,1,1,0.993711,1,1,0.987342,1,1,1,0.971519,1,1,1] area_under_roc_curve 0.9951402157471539 [1,1,1,1,1,1,1,1,1,1,0.993671,0.946203,1,1,1,1,1,1,1,1,1,1,0.962264,1,0.962025,1,1,0.974843,0.993711,1,1,1,1,1,1,0.987342,0.993671,1,0.993671,1,1,1,0.996835,1,0.949367,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993671,1,1,1,1,0.996835,1,1,1,1,0.990506,0.987421,1,1,1,0.908228,1,1,1,1,1,1,1,0.993711,0.977848,0.987421,1,1,0.993711,1,1,1,1,0.968553,1,1,0.993711,1,1,1,1,1] area_under_roc_curve 0.9866785088766816 [0.955975,1,1,1,0.981132,1,1,1,0.981013,1,0.996835,1,1,1,1,1,0.987421,1,1,0.952532,1,0.993711,1,1,0.740506,0.993711,0.838608,1,1,1,1,1,0.664557,1,1,0.990506,1,1,1,1,1,0.987342,1,0.955975,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,1,1,0.996835,1,1,1,1,1,1,0.990506,1,1,1,1,1,0.993711,1,1,1,0.987421,1,1,0.993671,0.962025,1,1,0.993671,0.993711,0.974684,1,0.937107,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,1] area_under_roc_curve 0.9962498009712604 [1,1,1,0.993671,0.993671,1,1,1,1,1,1,0.987342,1,1,0.996835,0.993671,0.993711,1,1,1,1,0.996835,1,1,0.987342,1,1,1,1,1,1,0.996835,0.993711,0.993711,1,1,0.993671,1,1,1,1,1,1,0.880503,1,1,1,1,0.905063,1,1,1,1,0.996835,0.996835,0.993711,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.968553,1,0.996835] area_under_roc_curve 0.9943582324625255 [1,0.993631,1,0.984076,0.792994,1,0.993631,1,1,1,1,0.996815,0.996815,1,1,0.996815,1,1,1,0.996815,1,0.996815,1,1,0.952229,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977707,0.990446,0.917722,1,1,0.93038,1,1,1,1,1,1,1,0.993671,1,1,0.993631,1,1,1,1,0.996815,1,1,0.996815,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993631,0.990446,1,1,1,1,1,1,1,1,1,1,1,1,0.993631,1,1,1,1] 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 0.8547062539481995 kappa 0.797876120168963 kappa 0.7978521794061908 kappa 0.7978202495656295 kappa 0.8230857323381905 kappa 0.8420657796027955 kappa 0.8610069101678184 kappa 0.7915268290756899 kappa 0.7915679772619612 kappa 0.8473783146022478 kb_relative_information_score 93.52132542539532 kb_relative_information_score 94.09635303895115 kb_relative_information_score 96.56367481386745 kb_relative_information_score 96.14694623243811 kb_relative_information_score 93.66912516821445 kb_relative_information_score 96.44361185820597 kb_relative_information_score 95.76537459763718 kb_relative_information_score 90.41293337045278 kb_relative_information_score 93.6750852296591 kb_relative_information_score 96.15231045024024 mean_absolute_error 0.01613614236622925 mean_absolute_error 0.016063466233733028 mean_absolute_error 0.0158234713594784 mean_absolute_error 0.015623181771948772 mean_absolute_error 0.015852532948483884 mean_absolute_error 0.015873193212027428 mean_absolute_error 0.01569920945200548 mean_absolute_error 0.016293444325168275 mean_absolute_error 0.016150833210530076 mean_absolute_error 0.015654261005221642 mean_prior_absolute_error 0.01980002942907592 mean_prior_absolute_error 0.01980002942907592 mean_prior_absolute_error 0.019800029429075917 mean_prior_absolute_error 0.019800029429075917 mean_prior_absolute_error 0.01980002942907591 mean_prior_absolute_error 0.01979995585638609 mean_prior_absolute_error 0.01979995585638609 mean_prior_absolute_error 0.01979995585638609 mean_prior_absolute_error 0.019799955856386102 mean_prior_absolute_error 0.01979995631910742 number_of_instances 160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,1,2,2,2,2,1,1,2,2,1,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,2,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1] number_of_instances 160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,2,2,2,2,2,2,1,2,2,2,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,1,2,1,2,2,2,2,2,2,2,1,1,2,2,1,1,2,1] number_of_instances 160 [2,1,2,2,2,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,1,1,2,2,2,1,2,1,1,1,1,2,1,1,1,2,2,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1] number_of_instances 160 [2,1,2,2,2,1,2,2,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,1,1,1,2,2,2,2,2,1,1,1,1,2,1,1,1,2,1,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,1,1,2,2,2,1] number_of_instances 160 [2,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,2,2,1,2,2,1,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,2,1,1,2,2,2,1,2,1,1,2,2,1,1,1,2,2,2,2] number_of_instances 160 [1,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,1,2,1,2,2,2,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,1,2,1,2,2,2,1,2,1,1,2,2,2,1,1,2,2,2,2] number_of_instances 160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,1,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,1,2,2,2,1,2] number_of_instances 160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,1,1,1,2,2,1,2,1,1,1,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,2,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,2,2,2,2,1,2] number_of_instances 160 [1,2,1,2,2,2,2,2,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,1,1,2,2,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,1,2,2,2,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2] number_of_instances 159 [1,2,1,2,2,2,2,1,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,2,1,2,1,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,2,1,1,1,2] predictive_accuracy 0.85625 predictive_accuracy 0.8 predictive_accuracy 0.8 predictive_accuracy 0.8 predictive_accuracy 0.825 predictive_accuracy 0.84375 predictive_accuracy 0.8625 predictive_accuracy 0.79375 predictive_accuracy 0.79375 predictive_accuracy 0.8490566037735848 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 prior_entropy 6.6438309601245775 recall 0.85625 [1,1,0,1,0.5,1,0,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1] recall 0.8 [1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0.5,1,0.5,1,1,0,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1] recall 0.8 [1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,0,0,1,1,1,1,1,1,1,1,0,1,0,0,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,0.5,0,1,0.5,0,1,0,1,1,1,1,1,1,1,1] recall 0.8 [1,0,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,1,0.5,1,0.5,0,1,1,0,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,0,1,1,0.5,1,1] recall 0.825 [0.5,1,1,1,1,1,1,0,0,1,1,0,0,1,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,0.5,1] recall 0.84375 [1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,1,0,1,1,1,1,0.5,1,1,1,0,1,1,1] recall 0.8625 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,0,1,1,1,0.5,1,1,1,0,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1] recall 0.79375 [0,1,1,1,0,0.5,1,1,0,1,1,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,0.5,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,0,1,1,0.5,0,1,0,1,0,0.5,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1] recall 0.79375 [1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,0.5,1,0.5,0,0,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,0,1,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0,1,0.5] recall 0.8490566037735849 [1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,0.5,1,1,1,1,1,0,0,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,1,1,1,1,0.5,1,0,0.5,1,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] relative_absolute_error 0.8149554738809464 relative_absolute_error 0.8112849675942486 relative_absolute_error 0.7991640323646172 relative_absolute_error 0.7890484116658163 relative_absolute_error 0.8006317872035476 relative_absolute_error 0.8016782121717628 relative_absolute_error 0.7928911339942207 relative_absolute_error 0.8229030631860293 relative_absolute_error 0.8157004655806306 relative_absolute_error 0.7906209868814161 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949890883178135 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.09949853911503082 root_mean_prior_squared_error 0.0994985414402977 root_mean_squared_error 0.08316992163618234 root_mean_squared_error 0.08277138029346359 root_mean_squared_error 0.08176364811033615 root_mean_squared_error 0.08167695776933753 root_mean_squared_error 0.08279897646919979 root_mean_squared_error 0.08199335324092676 root_mean_squared_error 0.08177280557994783 root_mean_squared_error 0.08418967037520053 root_mean_squared_error 0.08356849336824018 root_mean_squared_error 0.08158846662031524 root_relative_squared_error 0.8358877761845033 root_relative_squared_error 0.8318822916279585 root_relative_squared_error 0.8217542189188279 root_relative_squared_error 0.8208829496555119 root_relative_squared_error 0.8321596431693996 root_relative_squared_error 0.82406590056698 root_relative_squared_error 0.821849308615575 root_relative_squared_error 0.846139763699127 root_relative_squared_error 0.8398966870420698 root_relative_squared_error 0.8199966093902081 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 1814.1330079999989 usercpu_time_millis 1596.5337770000474 usercpu_time_millis 1614.9437950000447 usercpu_time_millis 1642.956378000008 usercpu_time_millis 1647.6173469999935 usercpu_time_millis 1632.8015399999458 usercpu_time_millis 1631.2339959999917 usercpu_time_millis 1620.147363000001 usercpu_time_millis 1659.5577950000688 usercpu_time_millis 1697.36873100004 usercpu_time_millis_testing 141.90508500001897 usercpu_time_millis_testing 136.37681000000157 usercpu_time_millis_testing 136.67748400001756 usercpu_time_millis_testing 141.81930099999818 usercpu_time_millis_testing 145.829305999996 usercpu_time_millis_testing 137.43556499997567 usercpu_time_millis_testing 138.09224300001688 usercpu_time_millis_testing 137.35177199998816 usercpu_time_millis_testing 136.43970700002228 usercpu_time_millis_testing 140.68998499999452 usercpu_time_millis_training 1672.22792299998 usercpu_time_millis_training 1460.1569670000458 usercpu_time_millis_training 1478.2663110000271 usercpu_time_millis_training 1501.1370770000099 usercpu_time_millis_training 1501.7880409999975 usercpu_time_millis_training 1495.3659749999701 usercpu_time_millis_training 1493.1417529999749 usercpu_time_millis_training 1482.7955910000128 usercpu_time_millis_training 1523.1180880000466 usercpu_time_millis_training 1556.6787460000455