8958589
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)
6894194
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
3.5278090462444274
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.340938623630204
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.0021475318230621183
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
false
7650
tol
0.00023107581556458399
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
18832442
description
https://api.openml.org/data/download/18832442/description.xml
-1
18832443
predictions
https://api.openml.org/data/download/18832443/predictions.arff
area_under_roc_curve
0.9037247474747474 [0.872159,0.96875,1,1,0.9375,0.843434,0.999684,1,0.935922,0.96875,0.874684,0.999684,0.999369,0.812184,1,0.90625,1,0.683081,0.777778,0.560606,0.843434,0.96875,0.872475,1,0.905934,0.684975,0.746843,0.998737,0.936869,1,0.999684,1,0.968434,0.810606,0.875,0.96654,0.839015,0.620896,0.967487,0.999684,0.873106,0.999369,1,0.998737,0.96875,0.96875,0.905934,0.936553,0.967172,0.905934,0.874369,0.905303,0.90625,0.843119,0.874053,0.620896,1,0.968434,0.716856,0.842487,0.904987,0.968119,0.873106,0.968119,0.936553,0.841856,0.714646,1,0.905934,0.779987,0.873422,0.874684,0.841856,0.96875,0.905303,0.968434,0.968434,0.716225,0.96875,0.999684,0.905934,0.904987,0.936869,0.841225,0.936553,0.937184,0.96875,0.936869,0.843119,0.96875,0.811237,0.968434,0.968434,0.937184,0.96875,0.905303,0.936237,0.96654,0.624369,0.905619]
average_cost
0
f_measure
0.8093116702036344 [0.648649,0.967742,1,1,0.933333,0.785714,0.969697,1,0.8,0.967742,0.827586,0.969697,0.941176,0.740741,1,0.896552,1,0.333333,0.5,0.166667,0.785714,0.967742,0.666667,1,0.866667,0.4,0.470588,0.888889,0.875,1,0.969697,1,0.9375,0.625,0.857143,0.789474,0.52381,0.242424,0.857143,0.969697,0.705882,0.941176,1,0.888889,0.967742,0.967742,0.866667,0.848485,0.833333,0.866667,0.8,0.8125,0.896552,0.758621,0.774194,0.242424,1,0.9375,0.482759,0.709677,0.787879,0.909091,0.705882,0.909091,0.848485,0.666667,0.388889,1,0.866667,0.62069,0.727273,0.827586,0.666667,0.967742,0.8125,0.9375,0.9375,0.451613,0.967742,0.969697,0.866667,0.787879,0.875,0.628571,0.848485,0.903226,0.967742,0.875,0.758621,0.967742,0.666667,0.9375,0.9375,0.903226,0.967742,0.8125,0.823529,0.789474,0.363636,0.83871]
kappa
0.8074494949494949
kb_relative_information_score
1294.3343671761222
mean_absolute_error
0.003812499999999974
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
0.8190492697447032 [0.571429,1,1,1,1,0.916667,0.941176,1,0.736842,1,0.923077,0.941176,0.888889,0.909091,1,1,1,0.3,0.45,0.25,0.916667,1,0.6,1,0.928571,0.428571,0.444444,0.8,0.875,1,0.941176,1,0.9375,0.625,1,0.681818,0.423077,0.235294,0.789474,0.941176,0.666667,0.888889,1,0.8,1,1,0.928571,0.823529,0.75,0.928571,0.857143,0.8125,1,0.846154,0.8,0.235294,1,0.9375,0.538462,0.733333,0.764706,0.882353,0.666667,0.882353,0.823529,0.647059,0.35,1,0.928571,0.692308,0.705882,0.923077,0.647059,1,0.8125,0.9375,0.9375,0.466667,1,0.941176,0.928571,0.764706,0.875,0.578947,0.823529,0.933333,1,0.875,0.846154,1,0.714286,0.9375,0.9375,0.933333,1,0.8125,0.777778,0.681818,0.666667,0.866667]
predictive_accuracy
0.809375
prior_entropy
6.6438561897747395
recall
0.809375 [0.75,0.9375,1,1,0.875,0.6875,1,1,0.875,0.9375,0.75,1,1,0.625,1,0.8125,1,0.375,0.5625,0.125,0.6875,0.9375,0.75,1,0.8125,0.375,0.5,1,0.875,1,1,1,0.9375,0.625,0.75,0.9375,0.6875,0.25,0.9375,1,0.75,1,1,1,0.9375,0.9375,0.8125,0.875,0.9375,0.8125,0.75,0.8125,0.8125,0.6875,0.75,0.25,1,0.9375,0.4375,0.6875,0.8125,0.9375,0.75,0.9375,0.875,0.6875,0.4375,1,0.8125,0.5625,0.75,0.75,0.6875,0.9375,0.8125,0.9375,0.9375,0.4375,0.9375,1,0.8125,0.8125,0.875,0.6875,0.875,0.875,0.9375,0.875,0.6875,0.9375,0.625,0.9375,0.9375,0.875,0.9375,0.8125,0.875,0.9375,0.25,0.8125]
relative_absolute_error
0.1925505050505034
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.06174544517614213
root_relative_squared_error
0.6205650732203728
total_cost
0
area_under_roc_curve
0.8897380781784887 [0.990566,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,0.5,1,0.5,1,0.5,0.746835,0.990566,0.5,1,0.993711,1,1,0.5,1,0.996835,1,1,1,1,0.746835,0.5,0.75,0.993711,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.993711,1,0.75,1,1,1,1,0.996855,0.487421,1,1,0.5,1,1,0.996855,0.75,1,1,1,0.496835,1,1,0.5,0.75,1,1,1,0.996855,0.996835,0.75,0.5,1,1,1,0.996835,1,0.996855,1,0.75,0.75,1,0.5,1,0.746835,1,1,1,1,0.746835,0.996855,0.993671,0.5,1]
area_under_roc_curve
0.9211249104370671 [1,1,1,1,1,1,0.996835,1,1,1,0.75,1,1,0.75,1,1,1,0.5,0.75,0.496855,1,1,0.996855,1,1,0.5,0.493671,1,0.996835,1,1,1,1,1,0.75,0.996855,0.996855,0.996855,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.487421,1,1,0.996855,0.5,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993671,1,1,0.746835,0.75,0.996855,1,0.746835,1,1,0.996835,0.5,1,0.75,1,1,0.75,1,1,1,0.996835,0.5,0.496855]
area_under_roc_curve
0.892723509274739 [0.743671,1,1,1,1,1,1,1,0.996835,1,0.75,1,0.996855,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,0.743671,0.996835,0.996855,1,1,1,1,1,0.496835,1,0.996855,0.990566,0.987421,1,1,0.75,1,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,0.75,1,0.5,1,1,0.993711,0.496835,0.496855,1,0.75,0.5,0.996835,0.993671,0.5,1,0.5,0.5,1,0.996835,0.993711,1,1,1,1,0.496835,0.75,0.996835,1,1,0.996835,0.5,1,1,1,1,0.996835,0.5,1,1,1,1,1,1,1,0.746835,0.5,1]
area_under_roc_curve
0.8928429265185891 [0.75,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,0.75,1,1,1,0.5,0.487421,0.5,1,1,0.993671,1,0.5,0.75,0.75,1,1,1,1,1,1,0.75,1,0.993711,0.996855,0.484277,0.996855,1,0.996835,0.996855,1,0.996855,1,1,0.75,1,0.996835,1,1,1,1,0.75,1,0.75,1,1,0.993711,1,1,1,0.996835,1,0.75,0.75,0.487342,1,1,0.746835,0.5,0.75,0.490566,1,1,1,1,0.75,1,1,1,1,0.75,0.5,1,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.75,0.996835,1,0.75]
area_under_roc_curve
0.9179404506010667 [0.996835,1,1,1,1,0.75,1,1,1,1,1,1,1,0.496855,1,1,1,0.746835,0.993711,0.75,0.75,1,0.75,1,1,0.996835,0.496835,1,0.996855,1,1,1,1,0.746835,1,1,0.996835,0.746835,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.5,0.496855,0.75,0.996855,0.746835,0.5,1,0.996835,0.5,1,1,1,1,1,0.75,1,0.993711,1,0.75,0.75,0.996835,1,1,1,0.75,1,0.996835,0.75,1,1,1,1,1,0.993671,0.996855,1,1,1,1,1,1,0.75,1,1,0.75,1,0.996835,0.996855,0.496855,1]
area_under_roc_curve
0.9242496616511421 [0.75,1,1,1,1,0.75,1,1,0.996855,0.75,0.75,0.996835,1,1,1,1,1,0.996835,0.990566,0.5,0.75,0.75,1,1,0.996835,0.996835,0.746835,1,1,1,1,1,1,0.996835,1,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,1,0.5,1,0.993671,1,0.993711,1,1,0.5,0.996855,1,1,1,0.75,0.75,1,1,0.75,1,1,0.746835,1,1,1,0.75,1,0.993671,1,1,1,1,0.996835,1,0.496855,1,1,1,1,1,1,0.996855,0.5,1]
area_under_roc_curve
0.8894992436907886 [0.996835,1,1,1,1,0.75,1,1,0.996855,1,0.996855,1,1,1,1,0.75,1,0.993711,0.496835,0.5,0.746835,1,0.746835,1,0.75,0.5,0.490566,1,1,1,0.996835,1,1,0.996855,1,0.996835,0.484177,0.5,0.990506,1,0.996835,1,1,1,0.5,0.75,0.5,0.746835,1,1,1,0.996835,1,1,0.75,0.5,1,1,0.5,1,0.996835,0.996835,0.996855,0.996835,1,0.5,0.993711,1,1,1,0.996855,1,0.75,1,0.996835,1,1,0.5,1,1,0.75,1,1,0.996835,1,1,1,0.5,0.5,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1]
area_under_roc_curve
0.8960273863545896 [0.75,1,1,1,1,0.75,1,1,0.996855,1,1,1,0.996835,1,1,0.75,1,0.987421,0.75,0.5,0.75,1,0.996835,1,0.75,0.490566,1,1,0.5,1,1,1,1,1,0.75,1,0.990506,0.496835,1,0.996855,0.5,1,1,1,1,1,1,0.75,0.993711,0.5,0.496855,0.75,0.75,0.996855,1,0.5,1,1,1,0.990566,1,1,1,1,1,0.496855,0.996855,1,1,0.746835,1,1,1,1,0.996835,1,1,0.490566,1,1,0.75,1,0.75,1,0.75,0.75,1,0.496855,1,1,0.5,1,1,0.996855,1,0.996835,0.75,1,0.746835,1]
area_under_roc_curve
0.921244327680917 [0.993711,1,1,1,1,0.996855,1,1,0.75,1,1,1,1,1,1,1,1,0.993711,1,0.496855,1,1,0.996855,1,1,0.496855,0.993711,0.996835,1,1,1,1,1,0.996855,1,1,0.75,0.5,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.75,1,0.75,1,0.996835,0.996835,1,0.996855,0.746835,1,0.996835,0.496855,0.996855,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.996855,1,1,1,1,1,1,0.75,1,1,1,1,0.75,0.996855,1,0.75,0.5]
area_under_roc_curve
0.8929026351405142 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1,1,0.487421,0.75,0.496855,1,1,1,1,1,0.5,0.5,0.996835,0.75,1,1,1,1,0.996855,0.75,1,0.5,0.5,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,1,0.984277,1,1,0.746835,0.75,0.75,0.75,0.496835,1,0.993711,0.993711,0.496835,1,0.75,0.996855,1,0.5,0.496835,0.75,0.75,0.75,1,1,1,1,1,0.490566,1,0.496855,0.996835,0.996835,1,1,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,1,0.75,0.996855]
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
0.7790055248618785
kappa
0.8420782502270125
kappa
0.7852433776795231
kappa
0.7853027073960059
kappa
0.8357484107869073
kappa
0.848389134554643
kappa
0.7788833609729132
kappa
0.7915926583777383
kappa
0.8420844848006317
kappa
0.7853027073960059
kb_relative_information_score
124.92361590545715
kb_relative_information_score
134.9454399324694
kb_relative_information_score
125.92579830815835
kb_relative_information_score
125.92579830815838
kb_relative_information_score
133.94325752976815
kb_relative_information_score
135.9476223351706
kb_relative_information_score
124.92361590545711
kb_relative_information_score
126.92798071085957
kb_relative_information_score
134.94543993246936
kb_relative_information_score
125.92579830815838
mean_absolute_error
0.004375000000000002
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.004250000000000002
mean_absolute_error
0.004250000000000002
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.004375000000000002
mean_absolute_error
0.004125000000000002
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.004250000000000002
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
0.78125
predictive_accuracy
0.84375
predictive_accuracy
0.7875
predictive_accuracy
0.7875
predictive_accuracy
0.8375
predictive_accuracy
0.85
predictive_accuracy
0.78125
predictive_accuracy
0.79375
predictive_accuracy
0.84375
predictive_accuracy
0.7875
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
0.78125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0,0.5,1,0,1,1,1,1,0,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0,1,0.5,1,1,1,1,0.5,1,1,0,1]
recall
0.84375 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,0,0]
recall
0.7875 [0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,0,1,0.5,0,1,1,0,1,0,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,0,1]
recall
0.7875 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5]
recall
0.8375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1]
recall
0.85 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1]
recall
0.78125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.79375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1]
recall
0.84375 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0,0,1,0,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,0]
recall
0.7875 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,0.5,0,1,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
relative_absolute_error
0.2209595959595957
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.2146464646464644
relative_absolute_error
0.2146464646464644
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.2209595959595957
relative_absolute_error
0.20833333333333307
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.2146464646464644
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
0.06614378277661478
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.06519202405202651
root_mean_squared_error
0.06519202405202651
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.06614378277661478
root_mean_squared_error
0.06422616289332567
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.06519202405202651
root_relative_squared_error
0.6647700293478879
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.6552044942557468
root_relative_squared_error
0.6552044942557468
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.6647700293478879
root_relative_squared_error
0.6454972243679026
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.6552044942557468
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
447.383555000215
usercpu_time_millis
465.91230200010614
usercpu_time_millis
437.1430319997671
usercpu_time_millis
425.9862830001566
usercpu_time_millis
424.1706980001254
usercpu_time_millis
433.76612099996237
usercpu_time_millis
431.94678800000474
usercpu_time_millis
426.64554499992846
usercpu_time_millis
438.43212000001586
usercpu_time_millis
428.55655200014553
usercpu_time_millis_testing
55.35509700007424
usercpu_time_millis_testing
57.04646100002719
usercpu_time_millis_testing
54.18558899987147
usercpu_time_millis_testing
52.48921900010828
usercpu_time_millis_testing
53.41597100004947
usercpu_time_millis_testing
51.982814999973925
usercpu_time_millis_testing
52.70144499991147
usercpu_time_millis_testing
53.73119199998655
usercpu_time_millis_testing
52.06300600002578
usercpu_time_millis_testing
52.42701900010616
usercpu_time_millis_training
392.02845800014074
usercpu_time_millis_training
408.86584100007894
usercpu_time_millis_training
382.95744299989565
usercpu_time_millis_training
373.49706400004834
usercpu_time_millis_training
370.75472700007595
usercpu_time_millis_training
381.78330599998844
usercpu_time_millis_training
379.24534300009327
usercpu_time_millis_training
372.9143529999419
usercpu_time_millis_training
386.3691139999901
usercpu_time_millis_training
376.1295330000394