8958839
1935
Hilde Weerts
9954
Supervised Classification
8317
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)
6894443
categorical_features
[]
7644
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
7644
handle_unknown
"ignore"
7644
n_values
"auto"
7644
sparse
true
7644
threshold
0.0
7645
copy
true
7646
with_mean
false
7646
with_std
true
7646
C
111.00483735628511
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.5897239368660909
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.04786221131501912
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
true
7650
tol
0.0022850502104658217
7650
verbose
false
7650
axis
0
8316
categorical_features
[]
8316
copy
true
8316
fill_empty
0
8316
missing_values
"NaN"
8316
strategy
"mean"
8316
strategy_nominal
"most_frequent"
8316
verbose
0
8316
memory
null
8317
openml-python
Sklearn_0.19.1.
study_98
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
18832938
description
https://api.openml.org/data/download/18832938/description.xml
-1
18832939
predictions
https://api.openml.org/data/download/18832939/predictions.arff
area_under_roc_curve
0.9201388888888886 [0.874053,0.96875,1,0.937184,0.937184,0.842803,1,1,0.905934,0.9375,0.968119,0.968434,0.96875,0.905934,0.966225,1,1,0.747159,0.745896,0.619949,0.874369,0.96875,0.967172,1,0.937184,0.746528,0.74779,0.967172,0.937184,1,0.968434,0.96875,0.999369,0.809975,0.905619,0.936869,0.77904,0.685606,0.968119,1,0.936237,0.968119,1,0.905303,0.968434,0.96875,0.936869,0.968119,0.936237,0.905619,0.873422,0.905303,0.905934,0.842803,0.905303,0.748106,1,0.968434,0.841856,0.842172,0.999369,0.968434,0.842803,0.999369,0.968119,0.842487,0.717172,1,0.968119,0.905303,0.874369,0.968434,0.93529,0.96875,0.904987,0.96875,0.968434,0.810922,0.96875,0.999684,0.937184,0.936553,0.90625,0.810922,0.905619,0.968434,1,0.936553,0.937184,0.96875,0.936869,0.968434,0.937184,0.968434,0.96875,0.936869,0.936553,0.936553,0.811237,0.937184]
average_cost
0
f_measure
0.8429125762410946 [0.774194,0.967742,1,0.903226,0.903226,0.733333,1,1,0.866667,0.933333,0.909091,0.9375,0.967742,0.866667,0.769231,1,1,0.484848,0.432432,0.222222,0.8,0.967742,0.833333,1,0.903226,0.457143,0.516129,0.833333,0.903226,1,0.9375,0.967742,0.941176,0.588235,0.83871,0.875,0.5625,0.428571,0.909091,1,0.823529,0.909091,1,0.8125,0.9375,0.967742,0.875,0.909091,0.823529,0.83871,0.727273,0.8125,0.866667,0.733333,0.8125,0.533333,1,0.9375,0.666667,0.6875,0.941176,0.9375,0.733333,0.941176,0.909091,0.709677,0.5,1,0.909091,0.8125,0.8,0.9375,0.756757,0.967742,0.787879,0.967742,0.9375,0.645161,0.967742,0.969697,0.903226,0.848485,0.896552,0.645161,0.83871,0.9375,1,0.848485,0.903226,0.967742,0.875,0.9375,0.903226,0.9375,0.967742,0.875,0.848485,0.848485,0.666667,0.903226]
kappa
0.8402777777777778
kb_relative_information_score
1346.447852116581
mean_absolute_error
0.0031624999999999874
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
0.8478412757169994 [0.8,1,1,0.933333,0.933333,0.785714,1,1,0.928571,1,0.882353,0.9375,1,0.928571,0.652174,1,1,0.470588,0.380952,0.2,0.857143,1,0.75,1,0.933333,0.421053,0.533333,0.75,0.933333,1,0.9375,1,0.888889,0.555556,0.866667,0.875,0.5625,0.5,0.882353,1,0.777778,0.882353,1,0.8125,0.9375,1,0.875,0.882353,0.777778,0.866667,0.705882,0.8125,0.928571,0.785714,0.8125,0.571429,1,0.9375,0.647059,0.6875,0.888889,0.9375,0.785714,0.888889,0.882353,0.733333,0.583333,1,0.882353,0.8125,0.857143,0.9375,0.666667,1,0.764706,1,0.9375,0.666667,1,0.941176,0.933333,0.823529,1,0.666667,0.866667,0.9375,1,0.823529,0.933333,1,0.875,0.9375,0.933333,0.9375,1,0.875,0.823529,0.823529,0.714286,0.933333]
predictive_accuracy
0.841875
prior_entropy
6.6438561897747395
recall
0.841875 [0.75,0.9375,1,0.875,0.875,0.6875,1,1,0.8125,0.875,0.9375,0.9375,0.9375,0.8125,0.9375,1,1,0.5,0.5,0.25,0.75,0.9375,0.9375,1,0.875,0.5,0.5,0.9375,0.875,1,0.9375,0.9375,1,0.625,0.8125,0.875,0.5625,0.375,0.9375,1,0.875,0.9375,1,0.8125,0.9375,0.9375,0.875,0.9375,0.875,0.8125,0.75,0.8125,0.8125,0.6875,0.8125,0.5,1,0.9375,0.6875,0.6875,1,0.9375,0.6875,1,0.9375,0.6875,0.4375,1,0.9375,0.8125,0.75,0.9375,0.875,0.9375,0.8125,0.9375,0.9375,0.625,0.9375,1,0.875,0.875,0.8125,0.625,0.8125,0.9375,1,0.875,0.875,0.9375,0.875,0.9375,0.875,0.9375,0.9375,0.875,0.875,0.875,0.625,0.875]
relative_absolute_error
0.15972222222222132
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.05623610939600985
root_relative_squared_error
0.5651941652604373
total_cost
0
area_under_roc_curve
0.9116511424249664 [0.996855,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,1,1,1,1,0.75,0.496835,0.490566,0.5,1,0.996855,1,1,0.743671,0.743671,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,0.996835,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.75,1,1,1,0.5,1,1,0.5,0.75,1,1,1,1,1,0.75,0.5,1,1,1,0.996835,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.5,0.993671,0.746835,1]
area_under_roc_curve
0.9179404506010667 [1,1,1,0.996855,1,1,1,1,1,1,1,1,1,0.75,0.75,1,1,0.75,0.746835,0.487421,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.996835,0.75,0.496855,0.496855,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.75,1,0.496855,1,1,1,0.5,1,1,0.75,1,1,0.996835,0.75,1,1,1,0.996835,1,1,1,0.996855,1,1,0.993671,1,1,0.75,0.996835,1,1,0.75,1,1,0.996835,0.75,1,0.75,1,0.75,1,1,1,1,1,0.75,0.496855]
area_under_roc_curve
0.9368879866252685 [1,1,1,1,1,0.746835,1,1,1,1,1,0.75,1,1,0.996835,1,1,0.743671,1,0.496835,1,1,0.75,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.993711,1,0.993711,0.5,1,1,0.75,1,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.996855,0.496835,1,1,0.75,1,1,1,1,1,1,0.75,1,0.996835,0.996855,1,0.993711,1,1,0.746835,0.75,0.996835,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1]
area_under_roc_curve
0.9148156993869915 [0.746835,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,1,1,1,1,0.743671,0.493711,0.746835,1,1,0.993671,1,0.5,1,1,1,1,1,1,1,1,0.75,1,1,1,0.496855,1,1,1,0.996855,1,0.996855,1,1,1,1,0.996835,1,0.996835,1,1,0.75,1,0.743671,1,1,0.996855,1,0.996855,1,1,1,0.75,0.5,0.496835,1,1,0.746835,0.5,1,0.990566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996855,0.75,0.996835,0.993711,0.75]
area_under_roc_curve
0.9115715309290661 [0.75,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,0.496855,0.996855,1,1,0.746835,0.990566,0.743671,0.75,1,1,1,1,0.987342,0.5,1,1,1,1,0.5,1,0.496835,1,1,0.746835,0.5,1,1,0.996835,1,1,0.5,1,1,1,1,1,1,0.496855,0.496855,0.75,0.996855,0.496835,0.5,1,0.996835,0.746835,1,1,1,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,0.75,1,0.996835,1,1,1,1,1,1,0.746835,0.996855,1,1,1,1,1,1,0.75,1,1,0.75,1,0.996835,1,0.5,1]
area_under_roc_curve
0.921025396067192 [0.75,1,1,0.75,1,0.75,1,1,1,0.75,1,0.996835,0.5,1,0.993711,1,1,0.996835,1,0.75,0.75,0.75,0.996835,1,0.996835,0.746835,0.746835,0.5,1,1,1,1,1,0.996835,1,0.75,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,0.996835,1,0.75,0.75,1,1,1,1,0.996835,0.996835,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.75,1,1,0.996835,1,0.75,1,1,1,0.75,1,1,0.746835,1,1,0.996855,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,0.75,1,1,1,1,1,1,0.996835,1,1,1]
area_under_roc_curve
0.9242098559031922 [1,1,1,1,0.996855,1,1,1,1,1,1,1,1,1,1,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,1,1,1,0.746835,1,1,0.996855,1,1,0.493671,0.746835,0.993671,1,1,0.75,1,1,0.5,0.75,0.75,0.996835,1,1,1,0.996835,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1]
area_under_roc_curve
0.917920547727092 [0.75,1,1,1,1,0.75,1,1,1,1,1,1,1,1,0.996855,1,1,0.496855,0.984177,0.496835,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.5,0.746835,1,1,1,1,1,1,1,1,1,0.75,0.996855,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,1,1,0.996835,0.990566,1,0.996835,1,1,1,0.496855,0.5,1,1,0.996835,1,1,0.996835,1,0.996835,1,1,0.5,1,1,0.75,1,0.75,0.75,0.75,0.75,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.996835,1]
area_under_roc_curve
0.9369078894992435 [0.996855,1,1,0.75,1,0.996855,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.493711,1,0.496855,1,1,1,1,1,0.496855,0.996855,0.993671,1,1,1,1,1,0.996855,1,1,1,0.5,0.75,1,0.5,1,1,0.746835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.75,0.5,1,0.5,0.996835,1,1,1,1,0.996835,1,1,0.743671,1,0.996835,1,1,1,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1]
area_under_roc_curve
0.9085661969588406 [1,1,1,1,1,0.996855,1,1,0.75,0.5,0.996855,1,1,0.75,0.993671,1,1,0.5,0.5,0.990566,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.996835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.746835,1,1,1,1,0.996835,0.75,1,1,0.496835,1,0.993711,0.996855,0.5,1,0.75,1,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,0.996835,1,0.746835,1,1,1,0.996835,1,0.75,1,1,0.996855,0.75,1,1]
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
0.8231136731551308
kappa
0.8357354392892399
kappa
0.8736576121288693
kappa
0.8294445102451735
kappa
0.8230857323381905
kappa
0.8420533070088845
kappa
0.8483532106468683
kappa
0.835715978200774
kappa
0.8736326659558504
kappa
0.8168180023687327
kb_relative_information_score
131.9388927243657
kb_relative_information_score
133.94325752976818
kb_relative_information_score
139.95635194597543
kb_relative_information_score
132.94107512706694
kb_relative_information_score
131.93889272436573
kb_relative_information_score
134.94543993246936
kb_relative_information_score
135.9476223351706
kb_relative_information_score
133.94325752976815
kb_relative_information_score
139.95635194597548
kb_relative_information_score
130.93671032166446
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.0025000000000000005
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.003500000000000001
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.0025000000000000005
mean_absolute_error
0.003625000000000001
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
0.825
predictive_accuracy
0.8375
predictive_accuracy
0.875
predictive_accuracy
0.83125
predictive_accuracy
0.825
predictive_accuracy
0.84375
predictive_accuracy
0.85
predictive_accuracy
0.8375
predictive_accuracy
0.875
predictive_accuracy
0.81875
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
0.825 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.8375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0]
recall
0.875 [1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1]
recall
0.83125 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5]
recall
0.825 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,0,0,0.5,1,0,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1]
recall
0.84375 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1]
recall
0.85 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.8375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
recall
0.875 [1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1]
recall
0.81875 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.12626262626262608
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.17676767676767655
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.12626262626262608
relative_absolute_error
0.18308080808080782
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
0.05916079783099617
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.05
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.05916079783099617
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.05
root_mean_squared_error
0.06020797289396149
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.5025189076296057
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5945883900105629
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.5025189076296057
root_relative_squared_error
0.6051128953853288
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
732.6434490000793
usercpu_time_millis
750.8887540002434
usercpu_time_millis
735.7970789998944
usercpu_time_millis
730.4960480003047
usercpu_time_millis
738.8840529999925
usercpu_time_millis
746.2838589999592
usercpu_time_millis
772.7314179999212
usercpu_time_millis
747.2217060001185
usercpu_time_millis
735.8341849999306
usercpu_time_millis
738.4881670000141
usercpu_time_millis_testing
54.49739000005138
usercpu_time_millis_testing
55.78021600013017
usercpu_time_millis_testing
52.85320099983437
usercpu_time_millis_testing
55.283643000166194
usercpu_time_millis_testing
55.579932000000554
usercpu_time_millis_testing
56.19297899988851
usercpu_time_millis_testing
59.216022999862616
usercpu_time_millis_testing
54.93100500007131
usercpu_time_millis_testing
55.48565999993116
usercpu_time_millis_testing
58.470340999974724
usercpu_time_millis_training
678.1460590000279
usercpu_time_millis_training
695.1085380001132
usercpu_time_millis_training
682.9438780000601
usercpu_time_millis_training
675.2124050001385
usercpu_time_millis_training
683.3041209999919
usercpu_time_millis_training
690.0908800000707
usercpu_time_millis_training
713.5153950000586
usercpu_time_millis_training
692.2907010000472
usercpu_time_millis_training
680.3485249999994
usercpu_time_millis_training
680.0178260000393