4862713
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)
2898122
class_weight
null
6941
criterion
"gini"
6941
max_depth
8
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
41375
6941
splitter
"best"
6941
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"mean"
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
0.7501051034777056
6971
n_estimators
57
6971
random_state
44791
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
10659631
description
https://api.openml.org/data/download/10659631/description.xml
-1
10659632
predictions
https://api.openml.org/data/download/10659632/predictions.arff
area_under_roc_curve
0.9774206912878789 [0.953086,0.989662,0.995502,1,0.975063,0.988242,0.996686,0.997475,0.993529,0.999842,0.986703,0.996567,0.984099,0.950797,0.999763,0.967487,0.99858,0.955611,0.947759,0.954072,0.978654,0.983507,0.978575,0.999566,0.988952,0.963463,0.951586,0.977983,0.95423,0.999487,0.996922,1,0.986979,0.986111,0.971867,0.99925,0.969066,0.950284,0.998935,0.998224,0.988952,0.991556,0.99925,0.957347,0.997633,0.98327,0.996054,0.992543,0.978022,0.940183,0.959261,0.966619,0.982323,0.926669,0.961253,0.947522,1,0.980745,0.990372,0.962516,0.991359,0.951468,0.992464,0.988005,0.994831,0.974747,0.955926,0.998501,0.925229,0.94693,0.95715,0.998501,0.969263,0.99708,0.95202,0.977786,0.988597,0.926531,0.997869,0.999132,0.971473,0.993529,0.983625,0.993095,0.939493,0.986348,0.999961,0.960878,0.984059,0.988005,0.98331,0.999487,0.977904,0.966185,0.980548,0.986348,0.945825,0.984848,0.887784,0.979719]
average_cost
0
f_measure
0.5163994979978525 [0.25,0.5,0.666667,0.896552,0.47619,0.518519,0.692308,0.787879,0.6,0.857143,0.56,0.769231,0.625,0.482759,0.857143,0.408163,0.608696,0.060606,0.137931,0.146341,0.380952,0.645161,0.4375,0.857143,0.592593,0.294118,0.171429,0.325581,0.785714,0.857143,0.769231,0.857143,0.486486,0.173913,0.529412,0.583333,0.347826,0.285714,0.814815,0.666667,0.439024,0.588235,0.814815,0.344828,0.8,0.382979,0.740741,0.583333,0.5625,0.375,0.255319,0.551724,0.555556,0.125,0.571429,0.206897,1,0.62069,0.470588,0.166667,0.5,0.571429,0.551724,0.48,0.666667,0.357143,0.054054,0.774194,0.35,0.179104,0.27907,0.774194,0.413793,0.866667,0.482759,0.466667,0.608696,0.202899,0.785714,0.769231,0.457143,0.642857,0.411765,0.551724,0.324324,0.705882,0.933333,0.173913,0.611111,0.518519,0.333333,0.866667,0.482759,0.324324,0.545455,0.6,0.315789,0.451613,0.040816,0.296296]
kappa
0.48674242424242425
kb_relative_information_score
952.51732853629
mean_absolute_error
0.014172378339104825
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
0.5870008504535883 [0.175,0.583333,1,1,1,0.636364,0.9,0.764706,0.642857,1,0.777778,1,0.625,0.538462,1,0.30303,1,0.058824,0.153846,0.12,0.8,0.666667,0.4375,1,0.727273,0.277778,0.157895,0.259259,0.916667,1,1,1,0.428571,0.285714,0.5,0.875,0.571429,0.263158,1,1,0.36,0.555556,1,0.384615,0.857143,0.290323,0.909091,0.875,0.5625,0.375,0.193548,0.615385,0.5,0.125,0.526316,0.230769,1,0.692308,0.444444,0.15,0.75,0.526316,0.615385,0.666667,0.818182,0.416667,0.047619,0.8,0.291667,0.117647,0.222222,0.8,0.461538,0.928571,0.538462,0.5,1,0.132075,0.916667,1,0.421053,0.75,0.388889,0.615385,0.285714,0.666667,1,0.285714,0.55,0.636364,0.5,0.928571,0.538462,0.285714,0.428571,0.642857,0.272727,0.466667,0.030303,0.363636]
predictive_accuracy
0.491875
prior_entropy
6.6438561897747395
recall
0.491875 [0.4375,0.4375,0.5,0.8125,0.3125,0.4375,0.5625,0.8125,0.5625,0.75,0.4375,0.625,0.625,0.4375,0.75,0.625,0.4375,0.0625,0.125,0.1875,0.25,0.625,0.4375,0.75,0.5,0.3125,0.1875,0.4375,0.6875,0.75,0.625,0.75,0.5625,0.125,0.5625,0.4375,0.25,0.3125,0.6875,0.5,0.5625,0.625,0.6875,0.3125,0.75,0.5625,0.625,0.4375,0.5625,0.375,0.375,0.5,0.625,0.125,0.625,0.1875,1,0.5625,0.5,0.1875,0.375,0.625,0.5,0.375,0.5625,0.3125,0.0625,0.75,0.4375,0.375,0.375,0.75,0.375,0.8125,0.4375,0.4375,0.4375,0.4375,0.6875,0.625,0.5,0.5625,0.4375,0.5,0.375,0.75,0.875,0.125,0.6875,0.4375,0.25,0.8125,0.4375,0.375,0.75,0.5625,0.375,0.4375,0.0625,0.25]
relative_absolute_error
0.7157766837931717
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08130420946150066
root_relative_squared_error
0.8171380504856391
total_cost
0
area_under_roc_curve
0.9820996039328082 [1,0.987342,1,1,1,0.993711,0.996835,1,1,1,0.977848,0.993671,1,0.981013,1,0.836478,0.990506,0.943038,0.89557,0.974843,0.987421,0.968553,0.962264,1,1,0.977848,0.993671,0.987342,0.993671,0.993711,1,1,0.974684,0.974684,0.990506,0.993711,1,0.981132,1,1,1,1,1,0.901899,1,0.981013,0.987421,1,1,0.829114,0.993671,1,0.993711,0.946203,1,0.968553,1,1,1,0.968354,1,1,1,0.993671,1,0.993671,0.987342,1,1,0.918239,0.993671,1,0.996835,1,1,1,1,0.920886,1,1,0.981013,1,0.981132,1,0.924051,0.984177,1,0.996835,1,0.993711,0.984177,1,1,0.981013,1,1,0.993711,0.981013,0.810127,0.993711]
area_under_roc_curve
0.9825399550195046 [0.937107,0.993671,1,1,1,1,1,1,1,1,0.987342,1,1,0.927215,1,1,1,0.996835,0.971519,0.955975,1,0.968553,0.993711,1,1,0.962025,0.984177,0.946203,1,1,1,1,0.996835,0.996835,0.990506,1,1,0.893082,1,1,0.974843,1,1,0.996835,1,0.987342,1,0.993711,0.990506,1,0.996835,0.962025,1,0.990506,1,0.981132,1,1,0.993711,0.914557,0.993711,0.981132,1,0.943038,1,0.984177,0.971519,1,0.937107,0.962264,0.886076,1,0.962025,1,1,0.993671,0.990506,0.943038,1,1,0.996835,0.996835,1,1,0.996835,1,1,0.971519,0.993671,1,0.987342,1,0.987342,0.96519,0.949367,1,0.830189,0.984177,0.813291,0.993711]
area_under_roc_curve
0.9872063082159059 [0.993671,1,1,1,0.996835,1,0.993711,0.996835,0.996835,1,0.987342,1,0.993711,1,1,0.968354,1,0.968354,0.918239,0.958861,0.968553,1,0.996835,1,1,0.96519,0.933544,0.981132,1,1,1,1,0.984177,0.987342,0.987421,1,1,0.968553,1,1,0.993671,0.955975,1,1,0.993671,0.987421,0.996835,0.974843,0.984177,1,0.962025,1,1,0.977848,1,0.984177,1,0.993671,0.949686,0.993671,0.993711,1,1,0.968553,0.996835,0.943038,0.981013,1,0.937107,0.920886,0.981013,1,1,1,0.993711,0.977848,0.990506,0.981013,0.993671,1,1,0.996835,0.990506,1,0.965409,1,1,0.995253,0.993671,0.955975,1,1,0.955975,0.990506,0.993671,0.993711,0.971519,0.984177,0.981132,0.968354]
area_under_roc_curve
0.9809208562216384 [0.927215,1,0.987342,1,0.898734,0.955696,1,1,0.990506,1,0.990506,1,1,0.990506,1,0.984177,0.996835,0.981013,0.90566,0.936709,0.974843,1,0.996835,1,0.981132,0.990506,0.943038,0.987421,1,1,1,1,1,0.984177,0.993711,1,0.949686,0.874214,1,0.996835,0.990506,0.987421,1,0.993711,1,0.993711,0.993671,1,1,1,0.889241,1,0.993711,0.85443,1,0.981013,1,1,0.993711,0.971519,1,1,0.996835,0.993711,1,0.996835,0.955696,1,1,0.955696,0.96519,0.996835,0.962264,0.993671,0.987421,0.990506,0.993671,0.863924,0.996835,1,1,0.993671,1,0.993671,1,1,1,0.981013,1,1,1,1,0.949686,0.974684,1,1,0.933544,1,0.798742,1]
area_under_roc_curve
0.9807775555290185 [0.96519,1,1,1,1,0.993671,1,1,0.974843,1,1,0.993671,1,0.830189,1,0.993671,1,0.974684,0.962264,0.993671,1,1,0.977848,1,0.984177,0.990506,0.905063,0.987421,1,1,1,1,0.977848,0.971519,1,1,0.990506,0.908228,1,1,0.974684,1,1,0.993711,1,0.981132,0.996835,0.996835,0.987342,0.971519,0.90566,0.930818,0.990506,0.993711,0.993671,0.968354,1,0.977848,1,0.974843,1,1,0.993711,0.993711,0.984177,0.996835,0.955975,1,0.96519,0.911392,1,1,1,1,0.832278,0.987421,0.943038,0.984177,1,1,1,0.984177,0.996835,0.990506,0.842767,1,1,0.874214,1,1,0.987421,1,1,0.90566,0.949367,0.943396,0.993671,0.974843,0.949686,0.981013]
area_under_roc_curve
0.9812042233898571 [0.924051,0.987421,0.977848,1,1,0.996835,1,0.987342,1,1,1,0.984177,0.930818,1,1,0.993671,1,0.968354,0.981132,0.876582,0.996835,1,1,1,0.993671,0.943038,0.914557,1,1,1,1,1,1,0.993671,0.955975,1,0.943038,0.949367,0.996835,1,0.993671,0.996835,1,0.90566,0.993711,0.993711,1,0.990506,0.977848,0.996835,1,1,0.993671,0.993711,0.990506,0.955696,1,1,0.996835,1,0.990506,1,0.987421,1,1,0.974684,0.886792,1,0.987342,0.96519,0.886076,0.990506,1,1,0.993671,0.981132,0.996835,0.860759,1,1,0.886792,1,0.987342,0.990506,0.924528,1,1,1,0.993671,0.993671,0.962264,1,0.993711,1,0.993671,1,1,1,0.792453,0.981013]
area_under_roc_curve
0.9851136454103973 [0.996835,1,1,1,1,0.996835,0.993671,1,1,1,1,1,0.971519,1,1,0.96519,1,0.830189,0.917722,0.96519,0.987342,0.996835,0.990506,1,0.993671,0.798742,0.993711,0.990506,0.987421,1,0.987342,1,0.993711,1,1,1,0.955696,0.987342,1,0.993711,1,0.993671,1,0.993671,0.987421,0.971519,0.996835,0.996835,1,1,0.943396,1,1,0.993711,0.955696,0.971519,1,0.987342,0.996835,0.993711,0.993671,0.984177,1,0.993671,0.996835,0.993711,0.981132,0.993671,0.974684,0.971519,0.993711,1,1,1,1,0.981132,1,0.955975,1,0.987421,0.96519,1,0.977848,0.996835,0.996835,1,1,1,0.993711,0.996835,1,1,0.955696,0.955975,1,0.977848,0.96519,0.968553,0.860759,0.984177]
area_under_roc_curve
0.9799058096489134 [0.993671,0.993711,1,1,0.993711,1,0.993671,1,1,1,0.949686,1,1,1,1,0.990506,1,0.924528,0.943038,0.981013,0.933544,0.996835,0.996835,0.993671,0.990506,0.993711,0.968553,0.984177,0.584906,1,0.996835,1,1,0.993711,0.955696,1,0.936709,0.927215,1,1,0.996835,1,1,0.882911,1,0.993671,1,0.993671,0.968553,0.981132,0.955975,0.990506,0.958861,0.962264,0.987342,0.892405,1,1,0.990506,0.974843,0.996835,0.952532,1,1,1,0.949686,0.918239,0.996835,0.876582,0.981013,1,1,0.984177,1,0.981013,1,1,0.955975,1,1,0.990506,1,0.958861,0.996835,0.974684,1,1,0.962264,0.955975,1,0.993711,1,0.981013,1,1,0.990506,0.993671,1,0.949367,0.996835]
area_under_roc_curve
0.9868454442321474 [1,0.984177,1,1,0.949367,0.987421,1,1,0.987342,1,0.987421,1,0.996835,1,1,1,1,0.987421,0.990506,0.981132,1,1,0.937107,1,0.990506,1,0.981132,1,1,1,1,1,1,0.981132,0.971519,1,1,0.968354,1,0.987421,0.993711,0.996835,1,0.939873,0.996835,1,1,0.974684,0.993711,0.949686,0.977848,0.996835,0.996835,0.955696,0.933544,0.962264,1,1,1,0.977848,0.996835,0.984177,0.996835,0.990506,1,1,0.958861,1,0.889241,0.899371,1,0.993711,0.955696,1,1,0.971519,1,1,1,1,1,0.981132,1,0.993711,1,1,1,0.993671,1,0.958861,0.968354,0.996835,0.987342,0.984177,0.993711,0.981013,0.974843,1,0.974684,0.981132]
area_under_roc_curve
0.979827193296712 [0.893082,0.981013,1,1,1,1,1,1,1,1,1,1,0.933544,0.996835,1,0.955975,0.993711,0.981132,0.974684,0.937107,0.952532,0.971519,0.993711,1,0.993671,0.993711,0.930818,0.955696,0.952532,1,1,1,0.893082,1,1,1,0.968354,0.993671,1,1,1,1,1,0.990506,1,0.962025,1,1,1,1,0.97943,1,0.977848,0.916139,1,0.949686,1,1,0.984177,0.927215,0.993671,0.993671,0.984177,0.996835,0.993711,0.987421,0.981013,1,0.993671,0.987421,0.943396,1,1,0.993671,0.860759,0.968354,1,0.962264,1,1,0.93038,0.962264,0.899371,0.993711,0.974684,0.952532,1,0.974684,1,1,0.981013,1,1,0.977848,0.987421,0.977848,0.72956,0.974684,0.981013,1]
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
0.5073619389728812
kappa
0.4505624629958555
kappa
0.49461049472894536
kappa
0.48831332912192044
kappa
0.45672773215413776
kappa
0.5199747355123954
kappa
0.5135433941404091
kappa
0.4695923280318876
kappa
0.5202209508778852
kappa
0.44440059979480706
kb_relative_information_score
99.93214619062688
kb_relative_information_score
95.71492794900219
kb_relative_information_score
93.5741710071352
kb_relative_information_score
95.54642295740484
kb_relative_information_score
93.79978249591764
kb_relative_information_score
91.14552091828078
kb_relative_information_score
99.02279769511416
kb_relative_information_score
93.71133774095098
kb_relative_information_score
98.46737378309756
kb_relative_information_score
91.60284779875849
mean_absolute_error
0.013499338376885082
mean_absolute_error
0.014425200593199766
mean_absolute_error
0.014447516699043872
mean_absolute_error
0.01428198145498614
mean_absolute_error
0.01426201588896841
mean_absolute_error
0.014825576943124452
mean_absolute_error
0.013693053947221567
mean_absolute_error
0.013986510723573053
mean_absolute_error
0.013903512845905682
mean_absolute_error
0.014399075918140495
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
0.45625
predictive_accuracy
0.5
predictive_accuracy
0.49375
predictive_accuracy
0.4625
predictive_accuracy
0.525
predictive_accuracy
0.51875
predictive_accuracy
0.475
predictive_accuracy
0.525
predictive_accuracy
0.45
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
0.5125 [1,0,0.5,1,0,0,0.5,1,1,1,0.5,0,1,0,1,0,0.5,0,0,0,0,0,0,1,1,0.5,1,0.5,0.5,0,0.5,0.5,0.5,0,0.5,0,0,0,1,0.5,1,1,1,0,1,0,0,0,1,0.5,1,1,0,0,1,0,1,1,1,0,1,1,0.5,0.5,0,0.5,0,1,1,0,0.5,1,0,1,1,0.5,0.5,0,1,0.5,0.5,1,0,1,0.5,0.5,0.5,0.5,0.5,0,0,1,1,0.5,1,1,1,1,0,0]
recall
0.45625 [1,0,0,0,0,0,0.5,1,0.5,1,0,0.5,1,0.5,1,1,0,0,0.5,0,1,0,0,1,1,0,0.5,0,1,0,1,1,0.5,0,1,1,0,0,1,0.5,0,1,1,0,1,0.5,1,0,0.5,0.5,1,0.5,1,0.5,0,0,1,1,0,0,0,0,1,0.5,1,0,0,1,0,0,0,1,0,1,1,1,0.5,0.5,1,1,0,0.5,0,0,0.5,0.5,1,0,1,0,0,1,0,0,0,1,0,0,0,0]
recall
0.5 [1,1,1,1,0.5,1,0,1,0.5,1,0.5,1,1,0,0,0.5,0.5,0,0,0.5,0,0,0.5,1,1,0.5,0,0,0.5,1,1,1,1,0,0,0,1,0,1,0.5,1,0,1,1,0.5,1,0.5,0,0.5,0.5,0,0,1,0.5,1,0.5,1,0.5,0,0.5,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,1,1,1,0,0,1,0,0.5,1,0.5,0.5,0,0,1,1,0,0.5,0,0.5,1,0,0.5,1,0,0.5,0,0,0]
recall
0.49375 [0,0.5,0,1,0.5,0,1,1,0.5,0.5,0.5,1,1,0,1,0.5,0.5,0,0,0,0,1,1,1,0,0,0,1,1,1,1,0.5,0.5,0.5,1,0,0,0,0,0.5,1,0,0,0,1,1,0.5,1,1,0.5,0,0,1,0,1,0.5,1,0,0,0,1,0,0.5,0,0,1,0,1,1,0.5,0,0.5,0,0.5,0,0.5,0.5,0,0.5,0.5,1,0.5,1,0.5,1,1,1,0,1,1,0.5,1,0,0.5,1,1,0,1,0,0.5]
recall
0.4625 [0.5,1,0.5,0.5,0,0,1,0.5,0,0.5,1,0.5,1,0,0,1,0.5,0.5,0,1,0.5,1,0.5,1,0,0.5,0,0,1,0.5,1,1,0,0,1,0,0,0,0.5,0.5,0.5,0,1,1,1,0,1,1,0,0.5,0,0,0.5,0,1,0.5,1,0.5,0.5,0,0,1,0,0,0.5,0.5,0,0.5,0,0,1,1,1,1,0,0,0,1,0.5,1,1,0.5,0.5,1,0,0,0,0,1,0.5,0,0.5,1,0,0.5,0,0.5,0,0,0]
recall
0.525 [0,0,0,1,1,0.5,1,0.5,1,0.5,0,0,0,1,1,0.5,0.5,0,0,0,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,1,0,0,0.5,0.5,0,0.5,0.5,0.5,1,0,0,1,1,0.5,0.5,0,0.5,1,1,1,0,1,0,1,1,1,1,0.5,1,0,0,0.5,0,0,1,1,0.5,0,0.5,1,1,0,0,0.5,0,1,0,0,0.5,0.5,0.5,0,1,1,0,0.5,0,0,1,1,1,1,1,1,1,0,0.5]
recall
0.51875 [0.5,1,1,1,1,0.5,0.5,0.5,0,0.5,1,1,0,1,1,0,0,0,0,0,0,1,0,0.5,0.5,0,0,1,0,1,0.5,0,0,0,1,0.5,0.5,1,1,0,1,0.5,1,0.5,0,0.5,0.5,0.5,1,0,0,1,1,0,0.5,0,1,0,1,0,0.5,0.5,1,0,0.5,0,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0,0,0,0.5,1,0.5,0.5,0.5,1,1,0,0,0.5,1,1,0,0,1,0,0.5,0,0,0.5]
recall
0.475 [0,0,1,1,0,1,0,1,1,1,0,1,1,1,0,1,1,0,0,0,0.5,1,0.5,0,0.5,0,0,0,0,1,0.5,1,1,0,0.5,0.5,0,0.5,1,1,0,1,1,0,1,0.5,0.5,0,0,0,0,0,0.5,0,0.5,0,1,1,0,0,0,0.5,1,1,1,0,0,0,0,1,1,1,0.5,0,0.5,1,1,0,1,1,0.5,1,0,0.5,0,1,1,0,0,1,0,0.5,0,1,1,0.5,0,0,0,0]
recall
0.525 [1,0.5,1,1,0,0,0.5,1,0.5,1,0,1,0.5,1,1,1,1,0,0.5,0,0,0.5,0,0.5,1,1,0,1,1,1,0,1,1,0,0,1,0.5,0,0,0,0,0.5,0,0.5,0.5,1,1,0.5,1,0,0.5,0.5,0.5,0,0,0,1,0,0.5,0.5,0.5,0.5,0.5,0,1,1,0.5,0,0.5,0,1,0,0,1,0.5,0.5,0,1,1,1,1,0,1,1,0.5,1,1,0.5,1,0,0.5,0.5,0.5,0.5,1,0.5,0,1,0,0]
recall
0.45 [0,0.5,1,0.5,0.5,1,1,1,0.5,1,1,1,0,0.5,1,1,0,0,0,0,0,0.5,0,1,0,0,0,0,0.5,0,0.5,0.5,0,1,0.5,0.5,0,1,1,1,0,1,0.5,0.5,0.5,0.5,1,0.5,1,0,0,0.5,0,0,0.5,0,1,1,0.5,0,0.5,0.5,0,1,1,0,0,1,0.5,0,0,0,0,0.5,0.5,0,1,1,1,0.5,0,0,0,0,0.5,0.5,1,0,1,1,0,1,1,0,0,0.5,0,0,0.5,1]
relative_absolute_error
0.6817847665093464
relative_absolute_error
0.7285454845050374
relative_absolute_error
0.7296725605577702
relative_absolute_error
0.7213121946962684
relative_absolute_error
0.7203038327761812
relative_absolute_error
0.7487665122790115
relative_absolute_error
0.6915683811728053
relative_absolute_error
0.7063894304834863
relative_absolute_error
0.7021976184800838
relative_absolute_error
0.7272260564717411
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
0.0789476685746642
root_mean_squared_error
0.08180809554371081
root_mean_squared_error
0.0817671377716792
root_mean_squared_error
0.08185941582701657
root_mean_squared_error
0.08232751466498978
root_mean_squared_error
0.08332974326821468
root_mean_squared_error
0.07974314473149292
root_mean_squared_error
0.08102461719383212
root_mean_squared_error
0.08005390055228827
root_mean_squared_error
0.08208146326697262
root_relative_squared_error
0.793453923440888
root_relative_squared_error
0.8222022961576794
root_relative_squared_error
0.8217906550604739
root_relative_squared_error
0.8227180844118006
root_relative_squared_error
0.8274226547462203
root_relative_squared_error
0.8374954312039746
root_relative_squared_error
0.8014487596283876
root_relative_squared_error
0.8143280424670297
root_relative_squared_error
0.8045719731404998
root_relative_squared_error
0.8249497451511738
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
2335.837574
usercpu_time_millis
2050.0537259999996
usercpu_time_millis
2122.1149429999996
usercpu_time_millis
2011.9290050000006
usercpu_time_millis
2171.192028000002
usercpu_time_millis
2162.8867619999996
usercpu_time_millis
2017.0129160000022
usercpu_time_millis
2169.742200000002
usercpu_time_millis
2084.3363170000016
usercpu_time_millis
2204.006263000004
usercpu_time_millis_testing
81.79997800000027
usercpu_time_millis_testing
80.84314999999975
usercpu_time_millis_testing
70.06167299999966
usercpu_time_millis_testing
80.08237900000026
usercpu_time_millis_testing
73.30018300000063
usercpu_time_millis_testing
84.38974099999896
usercpu_time_millis_testing
71.41446600000023
usercpu_time_millis_testing
83.09562700000228
usercpu_time_millis_testing
80.15734900000027
usercpu_time_millis_testing
70.7624640000013
usercpu_time_millis_training
2254.0375959999997
usercpu_time_millis_training
1969.210576
usercpu_time_millis_training
2052.05327
usercpu_time_millis_training
1931.8466260000005
usercpu_time_millis_training
2097.8918450000015
usercpu_time_millis_training
2078.4970210000006
usercpu_time_millis_training
1945.598450000002
usercpu_time_millis_training
2086.6465729999995
usercpu_time_millis_training
2004.1789680000015
usercpu_time_millis_training
2133.2437990000026