8958161
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)
6893769
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
4.432881370625326
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.3574122377360255
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.0026375611899159507
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
false
7650
tol
0.0002689360060707767
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
18831590
description
https://api.openml.org/data/download/18831590/description.xml
-1
18831591
predictions
https://api.openml.org/data/download/18831591/predictions.arff
area_under_roc_curve
0.9157196969696971 [0.873106,0.96875,1,1,0.9375,0.874684,1,1,0.936553,0.96875,0.874684,0.999684,0.96875,0.780934,1,0.90625,1,0.714646,0.778409,0.559343,0.874684,0.96875,0.966856,1,0.937184,0.746212,0.747475,0.999053,0.937184,1,0.999684,1,0.968434,0.81029,0.90625,0.967487,0.839962,0.684028,0.967487,0.999684,0.873106,0.968119,1,0.998737,0.968434,0.96875,0.874684,0.999369,0.936237,0.905934,0.967803,0.905303,0.90625,0.843434,0.874053,0.654672,1,0.968434,0.811237,0.904987,0.936869,0.999369,0.842172,0.999369,0.936553,0.841856,0.778093,1,0.905934,0.842487,0.905619,0.874684,0.872475,0.96875,0.905303,0.968434,0.968434,0.716856,0.96875,0.999684,0.905934,0.936237,0.9375,0.904356,0.936869,0.937184,0.96875,0.967803,0.905619,1,0.842803,0.968119,0.968434,0.968119,0.96875,0.936869,0.935922,0.96654,0.686237,0.905934]
average_cost
0
f_measure
0.832965898384779 [0.705882,0.967742,1,1,0.933333,0.827586,1,1,0.848485,0.967742,0.827586,0.969697,0.967742,0.692308,1,0.896552,1,0.388889,0.529412,0.142857,0.827586,0.967742,0.810811,1,0.903226,0.444444,0.5,0.914286,0.903226,1,0.969697,1,0.9375,0.606061,0.896552,0.857143,0.564103,0.363636,0.857143,0.969697,0.705882,0.909091,1,0.888889,0.9375,0.967742,0.827586,0.941176,0.823529,0.866667,0.882353,0.8125,0.896552,0.785714,0.774194,0.384615,1,0.9375,0.666667,0.787879,0.875,0.941176,0.6875,0.941176,0.848485,0.666667,0.514286,1,0.866667,0.709677,0.83871,0.827586,0.666667,0.967742,0.8125,0.9375,0.9375,0.482759,0.967742,0.969697,0.866667,0.823529,0.933333,0.742857,0.875,0.903226,0.967742,0.882353,0.83871,1,0.733333,0.909091,0.9375,0.909091,0.967742,0.875,0.8,0.789474,0.461538,0.866667]
kappa
0.8314393939393939
kb_relative_information_score
1332.4172984787647
mean_absolute_error
0.0033374999999999837
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
0.8405477303558974 [0.666667,1,1,1,1,0.923077,1,1,0.823529,1,0.923077,0.941176,1,0.9,1,1,1,0.35,0.5,0.166667,0.923077,1,0.714286,1,0.933333,0.4,0.5,0.842105,0.933333,1,0.941176,1,0.9375,0.588235,1,0.789474,0.478261,0.352941,0.789474,0.941176,0.666667,0.882353,1,0.8,0.9375,1,0.923077,0.888889,0.777778,0.928571,0.833333,0.8125,1,0.916667,0.8,0.5,1,0.9375,0.714286,0.764706,0.875,0.888889,0.6875,0.888889,0.823529,0.647059,0.473684,1,0.928571,0.733333,0.866667,0.923077,0.6,1,0.8125,0.9375,0.9375,0.538462,1,0.941176,0.928571,0.777778,1,0.684211,0.875,0.933333,1,0.833333,0.866667,1,0.785714,0.882353,0.9375,0.882353,1,0.875,0.736842,0.681818,0.6,0.928571]
predictive_accuracy
0.833125
prior_entropy
6.6438561897747395
recall
0.833125 [0.75,0.9375,1,1,0.875,0.75,1,1,0.875,0.9375,0.75,1,0.9375,0.5625,1,0.8125,1,0.4375,0.5625,0.125,0.75,0.9375,0.9375,1,0.875,0.5,0.5,1,0.875,1,1,1,0.9375,0.625,0.8125,0.9375,0.6875,0.375,0.9375,1,0.75,0.9375,1,1,0.9375,0.9375,0.75,1,0.875,0.8125,0.9375,0.8125,0.8125,0.6875,0.75,0.3125,1,0.9375,0.625,0.8125,0.875,1,0.6875,1,0.875,0.6875,0.5625,1,0.8125,0.6875,0.8125,0.75,0.75,0.9375,0.8125,0.9375,0.9375,0.4375,0.9375,1,0.8125,0.875,0.875,0.8125,0.875,0.875,0.9375,0.9375,0.8125,1,0.6875,0.9375,0.9375,0.9375,0.9375,0.875,0.875,0.9375,0.375,0.8125]
relative_absolute_error
0.1685606060606049
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.05777110004145657
root_relative_squared_error
0.5806214017078684
total_cost
0
area_under_roc_curve
0.8959477748586895 [0.993711,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,0.5,1,0.5,1,0.75,0.746835,0.996855,0.5,1,0.993711,1,1,0.746835,0.75,0.996835,1,1,1,1,0.746835,0.496835,0.75,0.996855,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.993711,1,0.75,1,1,1,1,0.996855,0.493711,1,1,0.5,1,1,0.996855,0.75,1,1,1,0.746835,1,1,0.5,0.75,1,1,1,0.996855,0.996835,0.75,0.5,1,1,1,0.996835,1,0.996855,1,0.75,0.75,1,0.75,1,0.746835,1,1,0.996835,1,0.746835,0.496855,0.993671,0.496835,1]
area_under_roc_curve
0.9400326407133187 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.5,0.75,0.493711,1,1,0.996855,1,1,0.746835,0.493671,1,1,1,1,1,1,1,0.75,0.996855,0.996855,0.996855,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.75,1,0.496855,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.746835,1,1,1,0.746835,1,1,1,0.75,1,0.75,1,1,1,1,1,1,0.996835,0.75,0.496855]
area_under_roc_curve
0.9178807419791417 [0.746835,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.75,1,0.496835,1,1,0.75,1,1,0.743671,0.996835,1,1,1,1,1,1,0.496835,1,0.996855,0.993711,0.996855,1,1,0.746835,0.5,1,0.996855,0.996835,1,0.75,1,0.75,1,0.996835,1,1,0.75,1,0.75,1,1,1,0.746835,0.5,1,0.75,1,0.996835,0.993671,0.75,1,0.5,0.75,1,0.996835,0.993711,1,1,1,1,0.496835,0.75,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,0.996855,1,1,1,1,1,0.996835,0.5,1]
area_under_roc_curve
0.9022967916567148 [0.75,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,0.75,1,1,1,0.5,0.490566,0.5,1,1,0.996835,1,0.5,0.746835,1,1,1,1,1,1,1,0.75,1,0.996855,0.996855,0.484277,0.996855,1,1,0.996855,1,0.996855,1,1,0.75,1,0.996835,1,1,1,1,0.75,1,0.75,1,1,0.996855,1,1,1,0.996835,1,0.75,0.746835,0.490506,1,1,0.996835,0.5,0.75,0.490566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,1,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.75,0.996835,0.996855,0.75]
area_under_roc_curve
0.9242297587771672 [0.996835,1,1,1,1,0.75,1,1,1,1,1,1,1,0.496855,1,1,1,0.746835,0.996855,0.743671,1,1,1,1,1,0.996835,0.5,1,0.996855,1,1,1,1,0.746835,1,1,0.996835,0.746835,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.496855,0.75,1,0.746835,0.5,1,0.996835,0.75,1,1,1,0.5,1,0.75,1,0.496855,1,0.75,0.75,1,1,0.996855,1,0.75,1,0.996835,0.75,1,1,1,1,1,0.993671,0.996855,1,1,1,1,1,1,0.75,1,1,0.75,1,0.996835,0.996855,0.496855,1]
area_under_roc_curve
0.9241899530292172 [0.75,1,1,1,1,0.75,1,1,0.996855,0.75,0.75,0.996835,0.5,1,1,1,1,0.993671,1,0.5,0.75,0.75,0.993671,1,0.996835,0.993671,0.746835,1,1,1,1,1,1,0.996835,1,0.75,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,1,0.996835,1,1,1,0.75,1,1,1,0.993711,1,1,0.5,0.996855,1,1,1,1,0.75,1,1,0.75,1,1,0.75,1,1,1,0.75,1,0.996835,1,1,1,0.996855,0.996835,1,0.5,1,1,1,1,1,0.996835,0.996855,0.5,1]
area_under_roc_curve
0.9052623198789904 [0.996835,1,1,1,1,0.75,1,1,0.996855,1,1,1,1,1,1,0.75,1,0.993711,0.490506,0.5,0.746835,1,1,1,1,0.5,0.493711,1,1,1,0.996835,1,1,0.996855,1,1,0.490506,0.5,0.990506,1,0.996835,1,1,1,0.5,0.75,0.5,1,1,1,1,0.996835,1,1,0.75,0.5,1,1,0.5,1,0.993671,0.996835,0.996855,0.996835,1,0.5,0.996855,1,1,1,0.996855,1,1,1,0.996835,1,1,0.5,1,1,0.75,1,1,0.996835,1,1,1,0.5,0.5,1,0.996855,1,0.75,1,1,1,1,1,0.5,1]
area_under_roc_curve
0.9180996735928666 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,0.990566,0.996835,0.5,0.75,1,1,1,0.75,0.490566,1,1,0.5,1,1,1,1,1,1,1,0.993671,0.75,1,0.996855,0.5,1,1,1,1,1,1,1,0.996855,0.5,0.996855,0.75,0.75,0.996855,1,0.5,1,1,1,0.990566,1,1,1,1,1,0.496855,0.996855,1,1,0.746835,1,1,0.996835,1,0.996835,1,1,0.493711,1,1,0.75,1,0.75,1,0.75,0.75,1,0.996855,1,1,0.5,1,1,0.996855,1,1,1,1,0.746835,0.75]
area_under_roc_curve
0.9274938301090676 [0.996855,1,1,1,1,0.996855,1,1,0.75,1,1,1,1,1,1,1,1,0.493711,1,0.493711,1,1,1,1,1,0.496855,0.993711,0.996835,1,1,1,1,1,0.996855,1,1,0.75,0.5,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.993671,1,1,1,1,0.996835,1,1,0.746835,1,0.996835,0.496855,1,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,0.996835,1,1,1,1,1,1,1,0.75,0.996855,1,0.75,1]
area_under_roc_curve
0.9023365974046651 [1,1,1,1,1,1,1,1,1,1,0.996855,1,1,0.75,1,1,1,0.490566,0.5,0.493711,1,1,1,1,1,0.5,0.5,0.996835,0.75,1,1,1,1,0.996855,0.75,1,0.746835,0.5,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,1,0.75,1,0.75,1,0.993711,1,1,0.746835,0.75,0.75,1,0.496835,1,0.993711,0.993711,0.746835,1,0.75,0.996855,1,0.5,0.496835,0.75,0.75,0.75,1,1,1,1,1,0.490566,1,0.496855,1,0.996835,1,1,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.75,1,1]
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
0.7915762049500651
kappa
0.8799605133267522
kappa
0.8357289527720739
kappa
0.804231133564888
kappa
0.848365187174222
kappa
0.848365187174222
kappa
0.8104489989337756
kappa
0.8357873129909604
kappa
0.8546890424481737
kappa
0.8042234063548451
kb_relative_information_score
126.92798071085957
kb_relative_information_score
140.95853434867672
kb_relative_information_score
133.94325752976812
kb_relative_information_score
128.93234551626205
kb_relative_information_score
135.9476223351706
kb_relative_information_score
135.9476223351706
kb_relative_information_score
129.93452791896325
kb_relative_information_score
133.94325752976815
kb_relative_information_score
136.94980473787183
kb_relative_information_score
128.93234551626202
mean_absolute_error
0.004125000000000002
mean_absolute_error
0.0023750000000000004
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.0038750000000000013
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.003750000000000001
mean_absolute_error
0.0032500000000000007
mean_absolute_error
0.002875000000000001
mean_absolute_error
0.0038750000000000013
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
0.79375
predictive_accuracy
0.88125
predictive_accuracy
0.8375
predictive_accuracy
0.80625
predictive_accuracy
0.85
predictive_accuracy
0.85
predictive_accuracy
0.8125
predictive_accuracy
0.8375
predictive_accuracy
0.85625
predictive_accuracy
0.80625
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
0.79375 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0.5,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0,1]
recall
0.88125 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,0]
recall
0.8375 [0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0,1,0.5,1,1,1,0.5,1,0,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.80625 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5]
recall
0.85 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0.5,0,1,1,0.5,1,1,1,0,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1]
recall
0.85 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1]
recall
0.8125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.8375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,0,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.5,0.5]
recall
0.85625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1]
recall
0.80625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,0.5,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.20833333333333307
relative_absolute_error
0.11994949494949476
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.19570707070707044
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.18939393939393911
relative_absolute_error
0.1641414141414139
relative_absolute_error
0.14520202020202
relative_absolute_error
0.19570707070707044
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
0.06422616289332567
root_mean_squared_error
0.048733971724044825
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.06224949798994367
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.061237243569579464
root_mean_squared_error
0.0570087712549569
root_mean_squared_error
0.05361902647381805
root_mean_squared_error
0.06224949798994367
root_relative_squared_error
0.6454972243679026
root_relative_squared_error
0.48979484470438195
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.6256309946079566
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.6154574548966634
root_relative_squared_error
0.5729597091269403
root_relative_squared_error
0.5388914922357191
root_relative_squared_error
0.6256309946079566
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
439.5514859999139
usercpu_time_millis
422.35951200007094
usercpu_time_millis
432.28658399993947
usercpu_time_millis
419.2329139999629
usercpu_time_millis
430.16927500013935
usercpu_time_millis
440.2506070000527
usercpu_time_millis
440.86456100001215
usercpu_time_millis
445.5062880000469
usercpu_time_millis
422.4788809999609
usercpu_time_millis
428.692367000167
usercpu_time_millis_testing
54.68253099991216
usercpu_time_millis_testing
52.89536700001918
usercpu_time_millis_testing
55.10897399994974
usercpu_time_millis_testing
54.3793449999157
usercpu_time_millis_testing
55.299549000096704
usercpu_time_millis_testing
54.907459999981256
usercpu_time_millis_testing
55.618624999965505
usercpu_time_millis_testing
71.4745090000406
usercpu_time_millis_testing
53.82531400005064
usercpu_time_millis_testing
54.13916200006952
usercpu_time_millis_training
384.86895500000173
usercpu_time_millis_training
369.46414500005176
usercpu_time_millis_training
377.1776099999897
usercpu_time_millis_training
364.8535690000472
usercpu_time_millis_training
374.86972600004265
usercpu_time_millis_training
385.34314700007144
usercpu_time_millis_training
385.24593600004664
usercpu_time_millis_training
374.0317790000063
usercpu_time_millis_training
368.65356699991025
usercpu_time_millis_training
374.5532050000975