5957304
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)
3992045
class_weight
null
6941
criterion
"gini"
6941
max_depth
2
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
26028
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"
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n_values
"auto"
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sparse
false
6948
threshold
0.0
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algorithm
"SAMME.R"
6971
learning_rate
0.013567905059173263
6971
n_estimators
317
6971
random_state
29192
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
12838042
description
https://api.openml.org/data/download/12838042/description.xml
-1
12838041
predictions
https://api.openml.org/data/download/12838041/predictions.arff
area_under_roc_curve
0.9791804766414143 [0.979443,0.981061,0.994081,0.999961,0.985519,0.993647,0.998343,0.986151,0.98694,0.994318,0.987729,0.991162,0.976286,0.946575,0.989623,0.967132,0.989268,0.956163,0.937658,0.9651,0.967645,0.995305,0.964686,1,0.992898,0.945727,0.909091,0.993253,0.989347,0.99566,0.994634,0.99925,0.980074,0.965061,0.992188,0.972518,0.956834,0.938131,0.989702,0.995186,0.97167,0.996962,0.998461,0.992306,0.986979,0.994515,0.998303,0.978259,0.992345,0.988715,0.977904,0.979719,0.977865,0.936119,0.96437,0.967507,1,0.998895,0.992661,0.952454,0.994239,0.96658,0.990846,0.989149,0.994871,0.965515,0.958136,0.999803,0.978022,0.956163,0.944208,0.988952,0.949791,0.998895,0.948035,0.987216,0.997356,0.918008,0.999527,0.999921,0.96003,0.994003,0.98765,0.994752,0.988794,0.976562,0.999605,0.986703,0.997435,0.99566,0.995502,0.993884,0.976681,0.947364,0.994634,0.972972,0.962318,0.984336,0.91355,0.981021]
average_cost
0
kappa
0.48484848484848486
kb_relative_information_score
984.2405917911059
mean_absolute_error
0.014821278163433642
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
0.49
prior_entropy
6.6438561897747395
recall
0.49 [0.5,0.25,0.5625,0.9375,0.4375,0.375,0.8125,0.3125,0.5,0.8125,0.8125,0.125,0.0625,0.5625,0.625,0.5625,0,0,0,0,0,0.8125,0.3125,0.9375,0.5625,0,0,0.4375,0.3125,0.5625,0.75,0.8125,0.75,0.0625,0.875,0,0,0,0.5625,0.5,0.8125,0.6875,0.1875,0.625,0.6875,0.75,0,0.25,0.75,0.375,0.375,0.4375,0.5625,0.4375,0.25,0,0.9375,0.8125,0.6875,0.5,0.625,0.5,0.625,0.6875,0.75,0.5625,0,1,0.75,0.5,0.625,0.375,0.0625,0.9375,0.5,0.6875,0.625,0.5625,0.8125,0.875,0.5625,0.6875,0.6875,0.5,0.75,0.8125,0.9375,0.375,0.5625,0.1875,0.0625,0.0625,0.5625,0.5625,0.8125,0.625,0.4375,0.1875,0.375,0.5625]
relative_absolute_error
0.748549402193617
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08288083884519694
root_relative_squared_error
0.8329837719982749
total_cost
0
area_under_roc_curve
0.9753030212562692 [0.968553,0.968354,0.981013,1,0.993671,0.993711,0.996835,1,0.993671,1,1,0.981013,0.958861,0.908228,1,0.811321,1,0.936709,0.963608,0.993711,0.95283,0.993711,0.981132,1,0.993711,0.93038,0.898734,0.996835,0.946203,0.962264,0.981013,1,1,0.969937,1,1,0.965409,0.965409,1,0.987342,1,0.993711,1,1,1,0.996835,1,1,1,0.968354,0.96519,1,1,0.955696,1,0.959119,1,1,0.987421,0.911392,1,1,0.984177,0.981013,1,0.987342,0.952532,1,0.993711,0.880503,0.946203,0.993711,0.993671,1,0.955975,0.996835,1,0.863924,1,1,0.990506,1,1,1,0.958861,0.886076,1,0.987342,1,1,0.993671,1,0.996835,0.936709,1,0.939873,1,0.977848,0.791139,0.993711]
area_under_roc_curve
0.9845120810445026 [0.981132,0.993671,0.996835,1,1,1,1,0.981132,0.968354,1,0.990506,0.990506,0.96519,0.892405,0.993671,0.987421,0.990506,0.962025,0.912975,0.993711,0.959119,0.981132,1,1,1,0.955696,0.908228,1,1,1,1,1,1,0.982595,1,1,0.965409,0.965409,1,0.984177,1,1,1,0.987342,0.993671,0.996835,1,0.981132,1,1,1,0.968354,0.993711,0.990506,0.993711,0.965409,1,1,1,0.924051,1,0.993711,0.996835,0.990506,1,0.993671,0.946203,1,0.855346,0.993711,0.996835,1,0.990506,1,0.968553,0.984177,1,0.993671,1,1,0.987342,1,1,0.981132,1,1,1,0.990506,1,0.993711,0.987342,1,1,0.905063,1,0.996835,1,0.984177,0.901899,1]
area_under_roc_curve
0.9801798473449568 [0.990506,0.990506,1,1,0.955696,0.987342,1,1,0.993671,1,1,1,1,0.993671,0.936709,0.958861,1,0.971519,0.792453,0.963608,0.930818,0.993711,0.889241,1,1,0.966772,0.974684,1,0.990506,1,1,1,1,0.958861,1,0.990566,0.930818,0.955975,1,1,1,1,1,0.993711,0.974684,1,0.996835,1,0.968354,0.996835,0.996835,0.993711,1,0.952532,0.830189,0.97943,1,0.993671,0.987421,0.920886,1,0.90566,1,1,1,0.958861,0.933544,1,1,0.908228,1,1,0.974843,1,0.955975,1,1,0.943038,0.996835,1,0.993711,0.993671,0.990506,1,0.993711,1,1,1,0.996835,1,1,1,1,0.927215,1,0.987421,0.96519,0.971519,0.842767,1]
area_under_roc_curve
0.9807728285964495 [0.96519,0.996835,0.96519,1,0.958861,0.971519,1,0.977848,0.987342,1,1,1,0.993711,0.990506,1,0.996835,0.993671,0.977848,0.955975,0.96519,0.993711,1,0.977848,1,0.930818,0.946203,0.89557,1,1,1,1,0.993671,1,0.977848,0.943396,0.987421,0.962264,0.937107,1,1,0.996835,1,0.993711,1,1,1,1,1,0.990506,1,1,1,0.874214,0.958861,1,0.977848,1,1,1,0.987342,1,1,0.993671,1,1,0.987342,0.996835,1,0.993711,1,0.933544,0.939873,0.974843,1,0.987421,0.96519,1,0.787975,1,1,0.698113,1,1,0.987342,1,1,1,0.993671,0.996835,0.993711,1,1,0.974843,0.990506,0.996835,0.867925,0.936709,0.981013,1,0.993671]
area_under_roc_curve
0.9735667442878752 [0.993671,0.981132,1,1,0.993711,0.987342,0.993711,0.993671,0.968553,1,0.939873,1,0.993711,0.918239,0.993711,0.984177,0.993671,0.955696,0.955975,0.93038,0.96519,1,0.977848,1,1,0.887658,0.822785,1,1,1,0.987421,1,1,0.890823,1,0.969937,0.966772,0.890823,1,1,1,0.993671,1,0.993711,1,0.974843,1,0.996835,0.996835,0.996835,0.968553,0.949686,0.990506,0.968553,0.889241,0.966772,1,1,0.993671,1,0.974684,1,1,1,0.971519,0.990506,0.962264,1,0.946203,0.920886,0.968354,1,0.861635,1,0.81962,0.949686,0.993671,0.89557,1,1,1,1,0.993671,0.987342,1,1,1,0.981132,1,1,1,0.993671,1,0.943396,0.968354,1,0.996835,0.993711,0.899371,0.873418]
area_under_roc_curve
0.9809566813947928 [0.984177,1,1,1,1,0.993671,1,1,1,0.996835,1,0.987342,1,1,1,0.946203,1,0.962025,1,0.987342,0.97943,0.996835,0.943038,1,1,0.924051,0.952532,0.993711,1,1,1,1,0.863924,0.962025,0.937107,0.949367,0.925633,0.955696,0.993671,1,0.996835,1,1,0.987421,0.981132,1,1,0.987342,0.993671,0.977848,0.861635,0.993711,0.977848,1,0.987342,0.962025,1,0.996835,0.996835,1,0.990506,0.996835,0.987421,1,1,0.981013,0.968553,1,1,1,0.794304,0.987342,0.968553,1,0.981013,1,0.996835,0.927215,1,1,1,0.981013,1,0.996835,1,1,0.987421,0.987421,0.987342,0.993671,1,1,0.955975,0.955975,0.996835,1,0.990506,0.987421,0.861635,0.987342]
area_under_roc_curve
0.9855511354589603 [1,1,1,1,1,1,1,1,1,1,1,1,0.955696,0.987421,0.987421,0.984177,1,0.965409,0.958861,0.996835,0.966772,1,0.96519,1,0.993671,0.90566,0.893082,0.993671,1,1,1,1,1,0.918239,1,0.987342,0.962025,0.973101,0.990506,1,0.993671,1,1,0.996835,0.981132,0.996835,0.996835,0.924051,0.993711,1,0.981132,0.993671,1,1,0.990506,0.97943,1,1,0.981013,0.987421,1,0.914557,0.993711,0.949367,1,0.90566,0.949686,1,0.996835,0.996835,1,1,0.920886,0.993711,0.96519,1,1,0.987421,1,1,0.990506,1,1,0.996835,1,0.996835,1,0.91195,1,0.996835,0.993711,1,0.93038,0.943396,1,0.984177,0.993671,0.987421,0.996835,1]
area_under_roc_curve
0.9827444570495979 [1,0.987421,1,1,1,1,1,0.993671,0.955975,1,1,0.981132,0.987342,0.993711,0.993711,1,0.987421,0.899371,0.938291,0.950949,0.963608,1,0.96519,1,0.987342,0.940252,0.90566,0.984177,0.987421,0.996835,1,1,1,0.996855,1,0.976266,0.968354,0.912975,0.990506,1,0.993671,1,1,0.981013,1,0.993671,1,0.987342,1,0.968553,0.930818,0.955696,0.996835,0.993711,0.996835,0.981013,1,1,0.996835,1,1,0.952532,1,0.996835,1,0.72327,0.993711,1,0.993671,0.952532,1,1,0.947785,1,0.987342,1,1,0.993711,1,1,0.892405,1,0.946203,1,0.996835,1,1,1,1,1,0.993711,0.996835,0.984177,1,1,1,0.93038,0.993711,0.933544,1]
area_under_roc_curve
0.9805770340737201 [0.968553,0.917722,1,1,0.996835,0.993711,0.996835,0.993711,1,1,0.943396,0.987421,0.993671,0.987342,1,0.987421,1,0.955975,0.971519,0.90566,0.971519,1,0.930818,1,0.993671,1,0.943396,1,0.987342,1,1,1,1,1,1,0.971519,0.971519,0.96519,0.981013,1,0.679245,0.993671,1,1,0.952532,1,1,1,0.993711,1,0.993671,0.968354,0.96519,0.753165,0.993671,0.968553,1,1,1,0.920886,0.993671,1,1,1,1,0.993711,0.990506,1,0.990506,0.930818,0.993711,0.974843,0.914557,1,0.984177,0.981013,0.993711,1,1,1,0.996835,1,1,0.981132,0.996835,1,1,0.984177,0.993711,1,0.996835,0.962025,1,0.974684,1,0.984177,0.899371,0.987342,0.955696,1]
area_under_roc_curve
0.9864616919433165 [0.886792,0.996835,0.993711,1,1,1,1,0.874214,0.984177,1,1,1,0.962025,0.914557,1,1,1,0.959119,0.960443,0.933962,0.97943,1,0.987421,1,0.996835,0.993711,0.918239,0.990506,1,0.981132,1,1,1,0.959119,0.990506,0.958861,0.974684,0.916139,0.981013,1,1,0.996835,1,0.984177,1,1,1,0.968354,1,1,0.993671,0.993671,0.996835,0.952532,1,0.930818,1,1,1,0.984177,1,0.990506,0.990506,1,1,0.987421,0.968354,1,1,1,0.968553,1,0.962025,1,0.987342,1,1,1,0.993711,1,0.996835,0.974843,0.962264,1,0.987342,0.996835,1,1,1,0.993671,0.996835,1,1,0.952532,1,0.993671,0.943396,1,1,1]
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
0.4944707740916272
kappa
0.5136394141565671
kappa
0.5138313405153704
kappa
0.48220064724919093
kappa
0.41937519722309874
kappa
0.42591278290355655
kappa
0.48248658882928364
kappa
0.5078282131166936
kappa
0.48224151539068666
kappa
0.5264217214570425
kb_relative_information_score
94.99264621397754
kb_relative_information_score
100.90646241689765
kb_relative_information_score
98.14124394210239
kb_relative_information_score
97.18701996721538
kb_relative_information_score
94.31740793011645
kb_relative_information_score
97.3711924542342
kb_relative_information_score
100.52970743327009
kb_relative_information_score
99.44283908515622
kb_relative_information_score
97.79582638841158
kb_relative_information_score
103.55624595972513
mean_absolute_error
0.01489549563599927
mean_absolute_error
0.014689196201731846
mean_absolute_error
0.014764928323810706
mean_absolute_error
0.015101784091245662
mean_absolute_error
0.0149860247492107
mean_absolute_error
0.015069667362200025
mean_absolute_error
0.014455397383107407
mean_absolute_error
0.014728498487979132
mean_absolute_error
0.014996786924061367
mean_absolute_error
0.01452500247499037
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
0.5
predictive_accuracy
0.51875
predictive_accuracy
0.51875
predictive_accuracy
0.4875
predictive_accuracy
0.425
predictive_accuracy
0.43125
predictive_accuracy
0.4875
predictive_accuracy
0.5125
predictive_accuracy
0.4875
predictive_accuracy
0.53125
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
0.5 [0,0,0,1,0.5,0,1,0,0,1,1,0.5,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0.5,1,0.5,0.5,1,0,0,0,1,0.5,1,0,0,0.5,1,0.5,0,1,1,0,0.5,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,0.5,1,1,0,1,1,0,0.5,0,0,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0.5,0.5,0,0,0,1,0.5,1,0.5,1,0.5,0,1]
recall
0.51875 [0,0,0.5,1,0,1,0.5,0,0,1,1,0,0,0.5,0.5,1,0,0,0,0,0,0,1,1,1,0,0,0,0.5,1,1,1,0.5,0,1,0,0,0,1,0.5,1,1,0,0.5,0.5,1,0,0,0.5,0.5,0.5,0,1,0.5,0,0,1,1,1,0,0,1,0.5,0.5,1,1,0,1,0,1,1,1,0.5,1,1,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0,0,0,1,0,1,1,1,0,0,1]
recall
0.51875 [0.5,0.5,1,1,0,0.5,1,0.5,1,0.5,1,0,0,1,0.5,1,0,0,0,0,0,0,0.5,1,0,0,0,1,0.5,0.5,1,1,1,0,1,0,0,0,1,0,0.5,1,0,1,0.5,1,0,1,0.5,0.5,1,1,1,0,0,0,1,0.5,0,0,1,0,1,0,0.5,0,0,1,1,0.5,1,1,0,1,1,1,1,0.5,0.5,1,1,0.5,0.5,1,0,1,1,0,0.5,0,0.5,0,1,0.5,1,0,0,0,1,1]
recall
0.4875 [0,0.5,0.5,1,0.5,0,0,0.5,1,1,1,0,0,0.5,0.5,1,0,0,0,0,0,1,1,1,0,0,0,1,0,0.5,1,0.5,1,0,0,0,0,0,1,0.5,1,1,1,1,1,1,0,0,0.5,0,1,1,0,0,1,0,1,1,1,0.5,1,0,1,1,0.5,0.5,0,1,1,0.5,0,0,0,1,1,0.5,0.5,0.5,1,1,0,0.5,1,0.5,1,1,1,0.5,0,1,0,0,0,1,1,0,0,0,0,0]
recall
0.425 [1,0,1,0.5,0,0,1,0.5,0,0.5,0.5,0.5,0,0,0,0,0,0,0,0,0,1,0.5,1,1,0,0,1,0,0.5,0,1,0.5,0,1,0,0,0,0.5,1,1,0.5,0,1,1,0,0,0.5,1,0.5,0,0,0.5,0,0,0,1,1,0.5,1,0.5,1,1,1,0.5,0.5,0,1,0.5,0.5,0.5,0.5,0,0,0,0,0,0,1,1,1,1,0.5,0,1,1,1,0,1,0,0,0,1,0,0,1,0.5,0,0,0]
recall
0.43125 [0.5,1,0,1,1,0,1,0,1,0.5,1,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0.5,1,1,0.5,0,0,0,0,0,1,0.5,1,0,0,0,1,1,0,0,1,0.5,0,1,0,1,0,0,1,1,0.5,1,0.5,0.5,1,1,1,0.5,0,1,0.5,1,0.5,0,0,1,0,1,0.5,0.5,1,0,1,0.5,1,0,1,1,0,0,0,0,0,0,0,1,1,0,0.5,0,0,0.5]
recall
0.4875 [1,0,1,1,1,1,1,0,1,1,0,0,0,1,0,0.5,0,0,0,0,0,1,0,0.5,0.5,0,0,0,0,0.5,0.5,0,1,0,1,0,0,0,0.5,0,1,0.5,0.5,1,0,0.5,0,0,1,1,0,0.5,1,1,0.5,0,1,0.5,0,1,1,0,1,0,0.5,0,0,1,1,0.5,1,1,0,1,0.5,1,1,0,0,1,0,1,1,1,1,0.5,1,0,0,0,0,0,0,1,1,0.5,1,0,0.5,1]
recall
0.5125 [1,1,1,1,0,0.5,1,1,0,1,1,0,0,1,1,0.5,0,0,0,0,0,1,0,1,0.5,0,0,0,0,1,1,1,1,0,1,0,0,0,0,1,0.5,1,0,0,1,1,0,0,1,0,0,0.5,0.5,1,0,0,0.5,1,1,1,0.5,0.5,1,1,1,0,0,1,0.5,0,1,0,0,1,0.5,1,1,1,1,1,0,0,0.5,0.5,0.5,0.5,1,1,1,0.5,0,0.5,0,1,1,1,0.5,1,0.5,0]
recall
0.4875 [0,0,1,1,0.5,1,1,0,0.5,1,0,0,0.5,0.5,1,1,0,0,0,0,0,1,0,1,1,0,0,1,0.5,1,0.5,0.5,1,0,1,0,0,0,0.5,0,0,1,0,1,0,0.5,0,0.5,0,0,0,0,0,0.5,0,0,1,1,1,0,1,1,0,1,1,1,0,1,1,0,1,0,0,1,0.5,0,0,1,1,1,1,1,0,0,1,1,1,0,1,0.5,0,0,0.5,0.5,1,0.5,0,0,0.5,1]
recall
0.53125 [0,0,0,1,1,0,0.5,0,0.5,1,1,0,0,0.5,0.5,1,0,0,0,0,0,1,0,1,0.5,0,0,0.5,0.5,0,1,1,1,0,1,0,0,0,0,1,1,1,0.5,0.5,1,1,0,0,1,1,0,0.5,1,0.5,0.5,0,1,0,1,1,0.5,0,0,1,1,1,0,1,1,1,0,0,0,1,0.5,1,1,1,0,0.5,1,0,0,1,0.5,1,1,1,1,0,0,0,1,0.5,0,1,0,0.5,1,1]
relative_absolute_error
0.7522977593939012
relative_absolute_error
0.7418785960470617
relative_absolute_error
0.7457034506975091
relative_absolute_error
0.7627163682447292
relative_absolute_error
0.7568699368288221
relative_absolute_error
0.7610943112222222
relative_absolute_error
0.7300705749044133
relative_absolute_error
0.7438635599989447
relative_absolute_error
0.7574134810131992
relative_absolute_error
0.733585983585371
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
0.08366859201023796
root_mean_squared_error
0.08175777128292093
root_mean_squared_error
0.08234263654895979
root_mean_squared_error
0.08364105079157874
root_mean_squared_error
0.08436344461259097
root_mean_squared_error
0.08384385790032664
root_mean_squared_error
0.08223794571200317
root_mean_squared_error
0.0820902025599131
root_mean_squared_error
0.08367702714541335
root_mean_squared_error
0.0811216593376351
root_relative_squared_error
0.8409009891978388
root_relative_squared_error
0.8216965183064916
root_relative_squared_error
0.8275746353984985
root_relative_squared_error
0.8406241895355303
root_relative_squared_error
0.8478845206117992
root_relative_squared_error
0.8426624776704805
root_relative_squared_error
0.8265224528979731
root_relative_squared_error
0.8250375783500119
root_relative_squared_error
0.8409857654961195
root_relative_squared_error
0.8153033527089879
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
3964.2295190000004
usercpu_time_millis
4042.856301999999
usercpu_time_millis
4012.6456160000025
usercpu_time_millis
4037.949103000004
usercpu_time_millis
3961.771856999999
usercpu_time_millis
4070.4111120000025
usercpu_time_millis
4087.8596049999983
usercpu_time_millis
4050.0112979999976
usercpu_time_millis
4024.400425999993
usercpu_time_millis
4088.674925999996
usercpu_time_millis_testing
352.6635819999999
usercpu_time_millis_testing
348.5853599999995
usercpu_time_millis_testing
325.6683490000007
usercpu_time_millis_testing
337.85425300000327
usercpu_time_millis_testing
346.605555
usercpu_time_millis_testing
344.63532700000246
usercpu_time_millis_testing
327.96281999999974
usercpu_time_millis_testing
329.37155399999796
usercpu_time_millis_testing
351.219406999995
usercpu_time_millis_testing
336.733578999997
usercpu_time_millis_training
3611.5659370000003
usercpu_time_millis_training
3694.270941999999
usercpu_time_millis_training
3686.9772670000016
usercpu_time_millis_training
3700.094850000001
usercpu_time_millis_training
3615.1663019999987
usercpu_time_millis_training
3725.775785
usercpu_time_millis_training
3759.8967849999985
usercpu_time_millis_training
3720.6397439999996
usercpu_time_millis_training
3673.181018999998
usercpu_time_millis_training
3751.941346999999