6005829
1
Jan van Rijn
9954
Supervised Classification
6969
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
4040560
bootstrap
true
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
0.3463505634201669
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
5
6902
min_samples_split
15
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
31032
6902
verbose
0
6902
warm_start
false
6902
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
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12934749
description
https://api.openml.org/data/download/12934749/description.xml
-1
12934750
predictions
https://api.openml.org/data/download/12934750/predictions.arff
area_under_roc_curve
0.9953914141414141 [0.989347,0.999566,1,1,0.999527,0.995344,0.999763,0.999092,0.997041,0.999882,0.997475,0.999961,0.999605,0.985598,0.999882,0.998816,0.999961,0.985283,0.987926,0.973919,0.999053,1,0.994555,1,0.998816,0.962753,0.991477,0.998382,0.99858,0.999645,1,1,0.999369,0.991398,0.999171,0.998777,0.994555,0.993845,0.99783,0.999961,0.998619,0.999645,1,0.999053,0.998895,0.999882,0.999605,0.997988,0.99779,0.99858,0.997199,0.994003,0.998106,0.983152,0.997633,0.979561,1,0.999842,0.995068,0.985638,1,0.999053,0.994081,0.998619,0.99858,0.996094,0.978969,0.999961,0.995107,0.987966,0.99633,0.998619,0.994397,0.999448,0.968119,0.99925,0.999487,0.984691,1,0.999487,0.993766,0.99708,0.997869,0.995739,0.997159,0.983704,0.999921,0.994871,0.997909,1,0.999882,0.999408,0.998698,0.992779,0.999605,0.999605,0.996094,0.996212,0.965554,0.998619]
average_cost
0
f_measure
0.7908395814092273 [0.615385,0.888889,1,1,0.933333,0.6,0.969697,0.857143,0.75,0.967742,0.709677,0.9375,0.857143,0.521739,0.969697,0.83871,0.888889,0.615385,0.540541,0.518519,0.833333,1,0.727273,1,0.888889,0.210526,0.451613,0.769231,0.903226,0.969697,0.909091,1,0.848485,0.733333,0.882353,0.810811,0.628571,0.578947,0.9375,0.941176,0.727273,0.882353,1,0.888889,0.9375,0.903226,0.903226,0.83871,0.774194,0.903226,0.848485,0.62069,0.833333,0.538462,0.689655,0.307692,1,0.969697,0.666667,0.714286,0.941176,0.833333,0.666667,0.882353,0.789474,0.75,0.470588,0.914286,0.56,0.518519,0.62069,0.848485,0.717949,0.914286,0.740741,0.9375,0.888889,0.47619,0.941176,0.967742,0.714286,0.727273,0.774194,0.709677,0.75,0.83871,0.969697,0.717949,0.882353,1,0.888889,0.789474,0.758621,0.740741,0.914286,0.909091,0.787879,0.625,0.48,0.774194]
kappa
0.7986111111111112
kb_relative_information_score
1100.5813985440375
mean_absolute_error
0.014409128093272924
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
0.8043327451291168 [0.8,0.8,1,1,1,0.642857,0.941176,0.789474,0.75,1,0.733333,0.9375,0.789474,0.857143,0.941176,0.866667,0.8,0.8,0.47619,0.636364,0.75,1,0.705882,1,0.8,0.666667,0.466667,0.652174,0.933333,0.941176,0.882353,1,0.823529,0.785714,0.833333,0.714286,0.578947,0.5,0.9375,0.888889,0.705882,0.833333,1,0.8,0.9375,0.933333,0.933333,0.866667,0.8,0.933333,0.823529,0.692308,0.75,0.7,0.769231,0.4,1,0.941176,0.647059,0.833333,0.888889,0.75,0.714286,0.833333,0.681818,0.75,0.444444,0.842105,0.777778,0.636364,0.692308,0.823529,0.608696,0.842105,0.909091,0.9375,0.8,1,0.888889,1,0.833333,0.705882,0.8,0.733333,0.75,0.866667,0.941176,0.608696,0.833333,1,0.8,0.681818,0.846154,0.909091,0.842105,0.882353,0.764706,0.625,0.666667,0.8]
predictive_accuracy
0.800625
prior_entropy
6.6438561897747395
recall
0.800625 [0.5,1,1,1,0.875,0.5625,1,0.9375,0.75,0.9375,0.6875,0.9375,0.9375,0.375,1,0.8125,1,0.5,0.625,0.4375,0.9375,1,0.75,1,1,0.125,0.4375,0.9375,0.875,1,0.9375,1,0.875,0.6875,0.9375,0.9375,0.6875,0.6875,0.9375,1,0.75,0.9375,1,1,0.9375,0.875,0.875,0.8125,0.75,0.875,0.875,0.5625,0.9375,0.4375,0.625,0.25,1,1,0.6875,0.625,1,0.9375,0.625,0.9375,0.9375,0.75,0.5,1,0.4375,0.4375,0.5625,0.875,0.875,1,0.625,0.9375,1,0.3125,1,0.9375,0.625,0.75,0.75,0.6875,0.75,0.8125,1,0.875,0.9375,1,1,0.9375,0.6875,0.625,1,0.9375,0.8125,0.625,0.375,0.75]
relative_absolute_error
0.7277337420844898
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.0766440552292637
root_relative_squared_error
0.770301738202255
total_cost
0
area_under_roc_curve
0.9936748666507444 [1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,1,1,0.971519,0.971519,0.993711,1,1,0.987421,1,1,0.987342,0.990506,1,0.993671,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.984177,1,1,1,1,1,1,1,0.987421,0.990506,1,1,0.993671,1,1,1,0.993671,1,1,0.943396,0.990506,1,1,1,1,1,1,0.981013,1,1,0.996835,1,0.993711,1,1,0.876582,1,0.996835,0.993671,1,1,1,1,0.977848,1,1,1,1,0.882911,1]
area_under_roc_curve
0.995614650505533 [0.981132,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,1,1,0.984177,1,0.987421,1,1,1,1,1,0.955696,0.993671,0.996835,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.968354,1,1,1,0.955975,1,1,1,0.927215,1,1,1,0.996835,1,1,0.996835,1,0.968553,1,1,1,1,1,1,1,1,0.984177,1,0.993671,1,0.996835,0.987421,0.993711,1,1,1,1,0.996835,1,1,1,1,0.987342,1,1,1,0.990506,0.996835,1]
area_under_roc_curve
0.9948648097285249 [1,1,1,1,1,0.990506,1,1,0.990506,1,1,1,1,0.996835,1,1,1,0.993671,0.981132,0.920886,0.993711,1,0.962025,1,1,0.977848,1,1,1,1,1,1,1,0.974684,1,1,0.993711,0.987421,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,0.993671,1,1,1,1,0.987421,0.984177,1,0.987421,1,1,1,0.981013,0.974684,1,0.993711,0.974684,1,1,0.993711,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835,0.899371,1]
area_under_roc_curve
0.9953763135896819 [1,0.993671,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,0.996835,1,1,1,0.984177,0.937107,0.993671,1,1,1,1,0.993711,0.955696,0.990506,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.936709,1,0.993671,1,1,1,0.993671,1,1,1,1,0.996835,1,0.958861,1,1,0.974684,0.993671,0.996835,0.981132,1,1,0.996835,1,0.981013,1,1,0.987421,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,1,1,0.993711,0.993671,0.993671,1,0.993671]
area_under_roc_curve
0.9932467060743573 [1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.91195,1,1,1,1,1,0.993671,0.993671,1,1,1,1,0.93038,0.990506,1,1,1,1,1,1,0.974684,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.996835,0.993711,0.984177,0.968354,1,0.996835,0.996835,1,1,1,0.993711,1,0.996835,1,0.893082,1,0.990506,0.981013,1,1,1,1,0.857595,1,1,0.971519,1,1,1,1,1,0.993671,0.981132,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,1,0.981132,0.996835]
area_under_roc_curve
0.9953753184459835 [0.96519,1,1,1,1,0.987342,1,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.924051,1,1,0.990506,1,1,0.993671,0.977848,0.993711,1,1,1,1,0.984177,0.993671,0.993711,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,0.996835,0.974684,1,1,0.993671,1,1,1,0.987421,1,1,1,0.993711,1,0.996835,1,0.990506,0.993671,1,1,0.971519,1,1,0.987342,1,1,0.987421,0.996835,1,0.993671,0.993711,1,1,0.987421,0.993671,1,1,1,1,1,1,1,1,0.987421,0.993711,0.993671]
area_under_roc_curve
0.9962532839742057 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,0.993711,0.996835,0.981013,0.996835,1,1,1,0.993671,0.861635,0.993711,0.996835,1,1,1,1,1,0.993711,1,1,0.987342,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,1,1,1,1,1,0.996835,0.990506,1,1,0.996835,0.993711,1,1,1,0.987342,1,0.993711,0.968553,1,0.996835,1,1,1,1,1,0.996835,1,1,0.981132,1,1,0.990506,1,0.996835,1,1,1,1,0.937107,1,1,1,1,1,1,1,1,1,1,0.962025,1]
area_under_roc_curve
0.9966055648435634 [1,1,1,1,1,1,1,1,0.993711,1,0.993711,1,0.996835,1,1,0.993671,1,0.968553,0.993671,0.993671,1,1,1,1,1,0.993711,0.987421,0.996835,1,0.996835,1,1,1,1,1,1,0.984177,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,1,0.96519,1,1,0.996835,1,1,0.996835,0.993711,1,1,0.955975,0.987421,1,1,0.996835,1,1,1,1,0.990506,1,1,0.962264,1,1,0.971519,1,0.993671,1,0.996835,0.996835,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.974684,1,1,1]
area_under_roc_curve
0.9968429066157153 [0.955975,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.987421,1,1,0.993671,0.996835,1,1,1,0.993711,1,1,0.996835,1,0.971519,1,0.955975,1,1,0.993671,0.996835,1,1,1,1,1,1,0.990506,1,1,0.993711,1,0.987421,0.981013,1,1,0.996835,1,1,1,0.996835,0.987342,0.993711,1,0.987421,0.996835,1,1,0.993671,1,1,1,0.996835,1,0.996835,1,1,0.981132,1,0.981013,1]
area_under_roc_curve
0.9976728564604729 [0.955975,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.987342,1,1,1,0.993711,0.990506,0.981132,1,1,1,1,1,0.981132,1,1,1,1,1,1,1,0.987421,1,1,0.996835,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,1,1,1,1,0.990506,1,1,1,0.984177,1,0.993711,1,0.996835,1,1,0.993711,1,1,0.987342,1,0.993671,0.996835,1,1,1,1,1,0.981132,1,0.974843,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.7726106351900833
kappa
0.8547062539481995
kappa
0.7852603323727944
kappa
0.7914938988271532
kappa
0.8167963043392427
kappa
0.7409571947559628
kappa
0.8104564839677776
kappa
0.7851755321249456
kappa
0.8041383667667036
kappa
0.8230857323381905
kb_relative_information_score
109.6942010614935
kb_relative_information_score
109.78712159863142
kb_relative_information_score
109.42488572633503
kb_relative_information_score
109.5444488404042
kb_relative_information_score
109.03342941300635
kb_relative_information_score
108.99768324433263
kb_relative_information_score
111.57193841310624
kb_relative_information_score
110.67612609242939
kb_relative_information_score
110.17195912713404
kb_relative_information_score
111.679605027165
mean_absolute_error
0.01435447815110905
mean_absolute_error
0.014469442673816956
mean_absolute_error
0.014446224538791866
mean_absolute_error
0.014495464169558695
mean_absolute_error
0.014445290663004077
mean_absolute_error
0.01453816488498988
mean_absolute_error
0.014163938008516294
mean_absolute_error
0.014330298901577753
mean_absolute_error
0.01452484493615904
mean_absolute_error
0.01432313400520561
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
0.775
predictive_accuracy
0.85625
predictive_accuracy
0.7875
predictive_accuracy
0.79375
predictive_accuracy
0.81875
predictive_accuracy
0.74375
predictive_accuracy
0.8125
predictive_accuracy
0.7875
predictive_accuracy
0.80625
predictive_accuracy
0.825
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
0.775 [0,1,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0.5,1,0,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0,0,0.5,1,1,1,0,1,1,0.5,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1]
recall
0.85625 [0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0.5,1]
recall
0.7875 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,0.5,0.5,1,0.5,1,0.5,1,1,0,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0,1]
recall
0.79375 [1,1,1,1,0.5,0,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,0.5,1,0.5,1,0,0.5,0.5,0.5,0,1,1,1,1,0,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0.5,1,0]
recall
0.81875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,0.5,0,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5]
recall
0.74375 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,0,0,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0.5]
recall
0.8125 [1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,0,1,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0.5,1,0.5,1,1,0,1,1,0,1,0,1,1,0.5,0,1,1,1,0.5,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.7875 [0.5,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,0.5,1,0,0.5,0.5,1,1,0.5,1,1,0,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,1,1,1,0.5,1,1,0,1,1,0,0,0,1,0,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1]
recall
0.80625 [0,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0.5,1,0.5,0,0,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0,0.5,0.5,1]
recall
0.825 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0,0,1,1,1,0,0.5,1,1,1,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
relative_absolute_error
0.7249736439954053
relative_absolute_error
0.7307799330210573
relative_absolute_error
0.7296072999389819
relative_absolute_error
0.7320941499777106
relative_absolute_error
0.7295601344951541
relative_absolute_error
0.7342507517671644
relative_absolute_error
0.7153504044705187
relative_absolute_error
0.723752469776653
relative_absolute_error
0.7335780270787382
relative_absolute_error
0.7233906063235144
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
0.07637399024061411
root_mean_squared_error
0.07673599876592037
root_mean_squared_error
0.07697584959572151
root_mean_squared_error
0.0770871296443844
root_mean_squared_error
0.07685058142525394
root_mean_squared_error
0.07743169876299016
root_mean_squared_error
0.07552800152991627
root_mean_squared_error
0.0762852978905446
root_mean_squared_error
0.07698901985139305
root_mean_squared_error
0.07616488984067395
root_relative_squared_error
0.7675874829405516
root_relative_squared_error
0.7712258055143415
root_relative_squared_error
0.7736363970540561
root_relative_squared_error
0.7747548036239569
root_relative_squared_error
0.7723774045703736
root_relative_squared_error
0.7782178535656502
root_relative_squared_error
0.7590849764852143
root_relative_squared_error
0.7666960912831107
root_relative_squared_error
0.7737687631039212
root_relative_squared_error
0.7654859448492946
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
4350.693070000034
usercpu_time_millis
4329.778818999785
usercpu_time_millis
4286.774668000135
usercpu_time_millis
4272.129350000341
usercpu_time_millis
4313.749946999906
usercpu_time_millis
4319.927604999975
usercpu_time_millis
4309.580618999917
usercpu_time_millis
4330.564353000227
usercpu_time_millis
4314.602693000097
usercpu_time_millis
4336.344815999837
usercpu_time_millis_testing
39.685799999915616
usercpu_time_millis_testing
37.71422999989227
usercpu_time_millis_testing
37.82836000004863
usercpu_time_millis_testing
40.61602800015862
usercpu_time_millis_testing
38.02927600008843
usercpu_time_millis_testing
37.77404200013734
usercpu_time_millis_testing
38.26338899989423
usercpu_time_millis_testing
37.78559200009113
usercpu_time_millis_testing
37.974752000081935
usercpu_time_millis_testing
37.776484999994864
usercpu_time_millis_training
4311.007270000118
usercpu_time_millis_training
4292.064588999892
usercpu_time_millis_training
4248.946308000086
usercpu_time_millis_training
4231.513322000183
usercpu_time_millis_training
4275.720670999817
usercpu_time_millis_training
4282.153562999838
usercpu_time_millis_training
4271.317230000022
usercpu_time_millis_training
4292.778761000136
usercpu_time_millis_training
4276.627941000015
usercpu_time_millis_training
4298.568330999842