8958488
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)
6894094
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
146.7575815402708
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.6098645612544872
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.06153547214658849
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
false
7650
tol
0.002750795118732039
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
18832242
description
https://api.openml.org/data/download/18832242/description.xml
-1
18832243
predictions
https://api.openml.org/data/download/18832243/predictions.arff
area_under_roc_curve
0.9207702020202021 [0.874053,0.96875,1,0.934659,0.9375,0.842487,1,0.96875,0.874684,0.9375,0.968434,0.968434,0.96875,0.90625,0.965593,0.96875,1,0.747159,0.746528,0.651831,0.905619,0.9375,0.967172,1,0.937184,0.747159,0.74779,0.967172,0.936869,1,0.968434,0.96875,0.999369,0.809975,0.936869,0.936553,0.77904,0.71654,0.968119,0.968434,0.936237,0.968119,1,0.967803,0.937184,0.96875,0.936869,0.999369,0.935922,0.905934,0.874053,0.905303,0.905934,0.843119,0.936553,0.748106,1,0.968434,0.841856,0.873422,0.999369,0.968434,0.842803,0.999369,0.936869,0.842487,0.717172,1,0.968119,0.905303,0.874369,0.968119,0.93529,0.9375,0.905619,0.96875,0.968434,0.811237,0.96875,0.968434,0.937184,0.936869,0.90625,0.810922,0.905619,0.968434,1,0.936869,0.937184,0.96875,0.936869,0.999684,0.9375,0.968434,0.96875,0.937184,0.936553,0.967803,0.810922,0.937184]
average_cost
0
f_measure
0.8442617170606332 [0.774194,0.967742,1,0.717949,0.933333,0.709677,1,0.967742,0.827586,0.933333,0.9375,0.9375,0.967742,0.896552,0.731707,0.967742,1,0.484848,0.457143,0.285714,0.83871,0.933333,0.833333,1,0.903226,0.484848,0.516129,0.833333,0.875,1,0.9375,0.967742,0.941176,0.588235,0.875,0.848485,0.5625,0.466667,0.909091,0.9375,0.823529,0.909091,1,0.882353,0.903226,0.967742,0.875,0.941176,0.8,0.866667,0.774194,0.8125,0.866667,0.758621,0.848485,0.533333,1,0.9375,0.666667,0.727273,0.941176,0.9375,0.733333,0.941176,0.875,0.709677,0.5,1,0.909091,0.8125,0.8,0.909091,0.756757,0.933333,0.83871,0.967742,0.9375,0.666667,0.967742,0.9375,0.903226,0.875,0.896552,0.645161,0.83871,0.9375,1,0.875,0.903226,0.967742,0.875,0.969697,0.933333,0.9375,0.967742,0.903226,0.848485,0.882353,0.645161,0.903226]
kappa
0.8415404040404041
kb_relative_information_score
1348.4522169219833
mean_absolute_error
0.003137499999999988
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
0.8504637634523385 [0.8,1,1,0.608696,1,0.733333,1,1,0.923077,1,0.9375,0.9375,1,1,0.6,1,1,0.470588,0.421053,0.263158,0.866667,1,0.75,1,0.933333,0.470588,0.533333,0.75,0.875,1,0.9375,1,0.888889,0.555556,0.875,0.823529,0.5625,0.5,0.882353,0.9375,0.777778,0.882353,1,0.833333,0.933333,1,0.875,0.888889,0.736842,0.928571,0.8,0.8125,0.928571,0.846154,0.823529,0.571429,1,0.9375,0.647059,0.705882,0.888889,0.9375,0.785714,0.888889,0.875,0.733333,0.583333,1,0.882353,0.8125,0.857143,0.882353,0.666667,1,0.866667,1,0.9375,0.714286,1,0.9375,0.933333,0.875,1,0.666667,0.866667,0.9375,1,0.875,0.933333,1,0.875,0.941176,1,0.9375,1,0.933333,0.823529,0.833333,0.666667,0.933333]
predictive_accuracy
0.843125
prior_entropy
6.6438561897747395
recall
0.843125 [0.75,0.9375,1,0.875,0.875,0.6875,1,0.9375,0.75,0.875,0.9375,0.9375,0.9375,0.8125,0.9375,0.9375,1,0.5,0.5,0.3125,0.8125,0.875,0.9375,1,0.875,0.5,0.5,0.9375,0.875,1,0.9375,0.9375,1,0.625,0.875,0.875,0.5625,0.4375,0.9375,0.9375,0.875,0.9375,1,0.9375,0.875,0.9375,0.875,1,0.875,0.8125,0.75,0.8125,0.8125,0.6875,0.875,0.5,1,0.9375,0.6875,0.75,1,0.9375,0.6875,1,0.875,0.6875,0.4375,1,0.9375,0.8125,0.75,0.9375,0.875,0.875,0.8125,0.9375,0.9375,0.625,0.9375,0.9375,0.875,0.875,0.8125,0.625,0.8125,0.9375,1,0.875,0.875,0.9375,0.875,1,0.875,0.9375,0.9375,0.875,0.875,0.9375,0.625,0.875]
relative_absolute_error
0.15845959595959505
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.05601339125602009
root_relative_squared_error
0.5629557637320983
total_cost
0
area_under_roc_curve
0.9022171801608154 [0.996855,0.75,1,0.996855,0.75,0.996855,1,1,0.75,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.490566,0.5,1,0.996855,1,1,0.743671,0.743671,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,0.996835,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,1,0.5,0.75,0.996855,1,1,1,1,0.75,0.5,1,1,1,0.996835,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.5,0.993671,0.746835,1]
area_under_roc_curve
0.9274540243611176 [1,1,1,0.990566,1,1,1,1,1,1,1,1,1,0.75,0.75,1,1,0.75,0.746835,0.490566,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.996835,1,0.496855,0.496855,0.496855,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.496855,1,1,1,0.75,1,1,1,1,1,0.996835,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.75,0.75,1,1,1,0.75,1,1,0.996835,0.75,1,0.75,1,0.75,1,1,1,1,1,0.75,0.496855]
area_under_roc_curve
0.9369078894992435 [1,1,1,0.990566,1,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.743671,1,0.746835,1,0.5,0.75,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.993711,1,0.996855,0.5,1,1,0.75,1,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.996855,0.496835,1,1,0.75,1,1,1,1,1,1,0.75,1,0.996835,0.996855,1,0.996855,1,1,0.746835,0.75,0.996835,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1]
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
0.9179404506010668 [0.75,1,1,1,1,0.75,1,0.75,1,1,0.996835,1,1,0.5,0.987421,1,1,0.746835,0.993711,0.746835,1,1,1,1,1,0.993671,0.5,1,0.996855,1,1,0.5,1,0.746835,1,1,0.746835,0.75,1,1,0.996835,1,1,1,0.5,1,1,1,1,1,0.5,0.496855,0.75,0.996855,0.746835,0.5,1,0.996835,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.5,0.75,1,0.996835,1,1,1,1,1,1,0.746835,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,1,0.496855,1]
area_under_roc_curve
0.9178608391051668 [0.75,1,1,0.75,1,0.75,1,1,1,0.75,1,0.996835,0.5,1,0.993711,1,1,0.996835,1,0.75,0.75,0.75,0.996835,1,0.996835,0.746835,0.746835,0.5,1,1,1,1,1,0.746835,1,0.746835,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,0.996835,1,0.75,0.75,1,1,1,1,0.996835,0.996835,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.75,1,1,0.996835,1,0.75,1,1,1,0.75,1,1,0.746835,1,1,0.996855,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,0.75,1,1,1,1,1,1,0.996835,1,1,1]
area_under_roc_curve
0.921065201815142 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,1,1,1,0.746835,1,1,0.996855,1,1,0.493671,0.746835,0.993671,0.5,1,0.75,1,1,0.5,0.75,0.75,0.996835,1,1,1,0.996835,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1]
area_under_roc_curve
0.9179603534750417 [0.75,1,1,1,1,0.75,1,1,1,1,1,1,1,1,0.996855,1,1,0.496855,0.987342,0.496835,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.496835,0.746835,1,0.996855,1,1,1,1,1,1,1,1,0.993711,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,1,1,0.996835,0.990566,1,0.996835,1,1,1,0.496855,0.5,1,1,0.996835,1,1,0.996835,1,0.996835,1,1,0.5,1,1,0.75,1,0.75,0.75,0.75,0.75,1,0.5,1,1,1,1,1,1,1,1,1,1,0.996835,1]
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
0.9148953108828914 [1,1,1,0.993671,1,0.996855,1,1,0.75,0.5,1,1,1,0.75,1,1,1,0.5,0.5,0.990566,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.996835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,1,1,1,1,0.996835,0.75,1,1,0.496835,1,0.993711,0.996855,0.5,1,0.75,0.996855,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,0.996835,1,1,1,1,1,0.996835,1,0.75,1,1,0.996855,1,1,1]
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
f_measure
0.8599999999999997 [1,1,1,0.4,1,1,1,1,1,1,1,1,1,0.666667,0.666667,1,1,0.666667,0.5,0,1,1,0.666667,1,1,0.5,0,1,0.8,1,1,1,1,0.8,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0.8,1,1,1,1,0.666667,1,0,1,1,1,0.666667,1,1,1,1,1,0.8,0.666667,1,1,1,0.8,1,1,1,1,1,1,0.8,1,0.666667,0.666667,1,1,1,0.666667,1,1,0.8,0.666667,1,0.666667,1,0.666667,1,1,1,1,1,0.666667,0]
kappa
0.8041770302814956
kappa
0.8546947800679143
kappa
0.8736576121288693
kappa
0.8294445102451735
kappa
0.8357289527720739
kappa
0.8357354392892399
kappa
0.8420345944238212
kappa
0.8357224657426056
kappa
0.8736326659558504
kappa
0.8294512435846823
kb_relative_information_score
128.93234551626202
kb_relative_information_score
136.94980473787183
kb_relative_information_score
139.95635194597543
kb_relative_information_score
132.94107512706694
kb_relative_information_score
133.94325752976815
kb_relative_information_score
133.94325752976815
kb_relative_information_score
134.94543993246936
kb_relative_information_score
133.94325752976815
kb_relative_information_score
139.95635194597548
kb_relative_information_score
132.94107512706694
mean_absolute_error
0.0038750000000000013
mean_absolute_error
0.002875000000000001
mean_absolute_error
0.0025000000000000005
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.0025000000000000005
mean_absolute_error
0.003375000000000001
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]
precision
0.9005208333333332 [1,1,1,0.25,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,0,1,0.666667,1,1,1,1,0.666667,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0.666667,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,1,0]
predictive_accuracy
0.80625
predictive_accuracy
0.85625
predictive_accuracy
0.875
predictive_accuracy
0.83125
predictive_accuracy
0.8375
predictive_accuracy
0.8375
predictive_accuracy
0.84375
predictive_accuracy
0.8375
predictive_accuracy
0.875
predictive_accuracy
0.83125
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
0.80625 [1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.85625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0]
recall
0.875 [1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1]
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
0.8375 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1]
recall
0.8375 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1]
recall
0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,0.5,1,0,1,0.5,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.8375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.83125 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.19570707070707044
relative_absolute_error
0.14520202020202
relative_absolute_error
0.12626262626262608
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.12626262626262608
relative_absolute_error
0.17045454545454522
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
0.06224949798994367
root_mean_squared_error
0.05361902647381805
root_mean_squared_error
0.05
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.05
root_mean_squared_error
0.058094750193111264
root_relative_squared_error
0.6256309946079566
root_relative_squared_error
0.5388914922357191
root_relative_squared_error
0.5025189076296057
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.5025189076296057
root_relative_squared_error
0.5838742081211419
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
695.8635629998753
usercpu_time_millis
699.9872369999594
usercpu_time_millis
708.4778450000613
usercpu_time_millis
704.441679999718
usercpu_time_millis
702.2267860002103
usercpu_time_millis
701.183176000086
usercpu_time_millis
729.1788449999785
usercpu_time_millis
698.2064689998424
usercpu_time_millis
711.6606640001919
usercpu_time_millis
695.8046680001644
usercpu_time_millis_testing
54.735621999952855
usercpu_time_millis_testing
55.25433899993004
usercpu_time_millis_testing
54.41397599997799
usercpu_time_millis_testing
55.821033999791325
usercpu_time_millis_testing
55.31320700015385
usercpu_time_millis_testing
56.44821500004582
usercpu_time_millis_testing
61.11265200001981
usercpu_time_millis_testing
56.18159599998762
usercpu_time_millis_testing
55.2754590000859
usercpu_time_millis_testing
52.151928000057524
usercpu_time_millis_training
641.1279409999224
usercpu_time_millis_training
644.7328980000293
usercpu_time_millis_training
654.0638690000833
usercpu_time_millis_training
648.6206459999266
usercpu_time_millis_training
646.9135790000564
usercpu_time_millis_training
644.7349610000401
usercpu_time_millis_training
668.0661929999587
usercpu_time_millis_training
642.0248729998548
usercpu_time_millis_training
656.385205000106
usercpu_time_millis_training
643.6527400001069