8956754
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)
6892368
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
6.089200937852132
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.3803126460647601
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.0035098561751672245
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
true
7650
tol
0.0003320860131175292
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
18828794
description
https://api.openml.org/data/download/18828794/description.xml
-1
18828795
predictions
https://api.openml.org/data/download/18828795/predictions.arff
area_under_roc_curve
0.9191919191919191 [0.904356,0.968434,1,1,0.9375,0.874684,1,1,0.936869,0.96875,0.905934,0.999684,1,0.843434,1,0.90625,1,0.746528,0.716856,0.558712,0.905934,0.96875,0.966856,1,0.968119,0.714962,0.747159,0.967803,0.9375,1,0.999684,1,0.999369,0.779356,0.905619,0.967487,0.903093,0.714646,0.967803,0.999684,0.842487,0.968119,1,0.999053,0.968434,0.96875,0.905934,0.968119,0.904672,0.905934,0.936553,0.905619,0.90625,0.874053,0.811553,0.71654,1,0.968434,0.841856,0.904672,0.968119,0.968434,0.873422,0.999053,0.999369,0.842487,0.622159,1,0.874369,0.873737,0.905934,0.875,0.90404,0.96875,0.905303,0.968434,0.968434,0.81029,0.96875,0.999684,0.9375,0.905619,0.90625,0.904356,0.936869,0.937184,0.96875,0.936237,0.968434,1,0.874369,0.999369,0.968434,0.968119,0.96875,0.937184,0.936553,0.966856,0.686553,0.937184]
average_cost
0
f_measure
0.8398360917057656 [0.742857,0.9375,1,1,0.933333,0.827586,1,1,0.875,0.967742,0.866667,0.969697,1,0.785714,1,0.896552,1,0.457143,0.482759,0.133333,0.866667,0.967742,0.810811,1,0.909091,0.4,0.484848,0.882353,0.933333,1,0.969697,1,0.941176,0.580645,0.83871,0.857143,0.666667,0.388889,0.882353,0.969697,0.709677,0.909091,1,0.914286,0.9375,0.967742,0.866667,0.909091,0.764706,0.866667,0.848485,0.83871,0.896552,0.774194,0.689655,0.466667,1,0.9375,0.666667,0.764706,0.909091,0.9375,0.727273,0.914286,0.941176,0.709677,0.275862,1,0.8,0.75,0.866667,0.857143,0.722222,0.967742,0.8125,0.9375,0.9375,0.606061,0.967742,0.969697,0.933333,0.83871,0.896552,0.742857,0.875,0.903226,0.967742,0.823529,0.9375,1,0.8,0.941176,0.9375,0.909091,0.967742,0.903226,0.848485,0.810811,0.48,0.903226]
kappa
0.8383838383838383
kb_relative_information_score
1343.4413049084772
mean_absolute_error
0.0031999999999999867
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
0.8461717897890835 [0.684211,0.9375,1,1,1,0.923077,1,1,0.875,1,0.928571,0.941176,1,0.916667,1,1,1,0.421053,0.538462,0.142857,0.928571,1,0.714286,1,0.882353,0.368421,0.470588,0.833333,1,1,0.941176,1,0.888889,0.6,0.866667,0.789474,0.565217,0.35,0.833333,0.941176,0.733333,0.882353,1,0.842105,0.9375,1,0.928571,0.882353,0.722222,0.928571,0.823529,0.866667,1,0.8,0.769231,0.5,1,0.9375,0.647059,0.722222,0.882353,0.9375,0.705882,0.842105,0.888889,0.733333,0.307692,1,0.857143,0.75,0.928571,1,0.65,1,0.8125,0.9375,0.9375,0.588235,1,0.941176,1,0.866667,1,0.684211,0.875,0.933333,1,0.777778,0.9375,1,0.857143,0.888889,0.9375,0.882353,1,0.933333,0.823529,0.714286,0.666667,0.933333]
predictive_accuracy
0.84
prior_entropy
6.6438561897747395
recall
0.84 [0.8125,0.9375,1,1,0.875,0.75,1,1,0.875,0.9375,0.8125,1,1,0.6875,1,0.8125,1,0.5,0.4375,0.125,0.8125,0.9375,0.9375,1,0.9375,0.4375,0.5,0.9375,0.875,1,1,1,1,0.5625,0.8125,0.9375,0.8125,0.4375,0.9375,1,0.6875,0.9375,1,1,0.9375,0.9375,0.8125,0.9375,0.8125,0.8125,0.875,0.8125,0.8125,0.75,0.625,0.4375,1,0.9375,0.6875,0.8125,0.9375,0.9375,0.75,1,1,0.6875,0.25,1,0.75,0.75,0.8125,0.75,0.8125,0.9375,0.8125,0.9375,0.9375,0.625,0.9375,1,0.875,0.8125,0.8125,0.8125,0.875,0.875,0.9375,0.875,0.9375,1,0.75,1,0.9375,0.9375,0.9375,0.875,0.875,0.9375,0.375,0.875]
relative_absolute_error
0.16161616161616066
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.056568542494923685
root_relative_squared_error
0.5685352436149594
total_cost
0
area_under_roc_curve
0.9022370830347903 [0.993711,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,1,1,0.5,1,0.75,0.746835,0.493711,0.5,1,0.993711,1,1,0.746835,0.746835,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,1,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.75,1,1,1,0.496835,1,1,0.5,0.75,1,1,1,0.996855,0.996835,0.75,0.5,1,1,1,0.996835,1,0.996855,1,0.75,0.75,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.496855,0.993671,0.496835,1]
area_under_roc_curve
0.9337035267892683 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.746835,0.75,0.496855,1,1,0.996855,1,1,0.746835,0.490506,1,1,1,1,1,1,1,0.75,0.996855,0.996855,0.496855,1,1,1,1,1,1,1,1,1,1,0.746835,1,0.993671,1,1,0.75,1,0.496855,1,1,0.996855,0.75,1,1,1,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.75,0.75,1,1,0.746835,1,1,0.996835,1,1,0.75,1,1,1,1,1,1,0.996835,0.75,0.496855]
area_under_roc_curve
0.930538969827243 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.743671,1,0.490506,1,1,1,1,1,0.743671,1,1,1,1,1,1,1,0.496835,0.996855,0.996855,0.993711,0.5,1,1,0.746835,0.5,1,0.996855,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,0.75,1,1,1,0.746835,1,1,1,0.996855,1,1,0.5,1,0.5,0.75,1,1,0.996855,1,1,1,1,0.746835,0.75,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,1,1,1,1,0.996835,0.5,1]
area_under_roc_curve
0.9148356022609663 [0.75,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,0.75,1,1,1,0.743671,0.493711,0.5,1,1,0.996835,1,1,0.746835,1,1,1,1,1,1,1,0.75,1,0.996855,1,0.490566,1,1,1,0.996855,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.75,1,1,0.996855,1,1,1,0.996835,1,1,0.746835,0.490506,1,1,0.996835,0.5,0.75,0.490566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,1,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.75,0.996835,0.996855,0.75]
area_under_roc_curve
0.921005493193217 [0.996835,1,1,1,1,0.75,1,1,1,1,1,1,1,0.5,1,1,1,0.496835,0.996855,0.743671,1,1,1,1,1,0.996835,0.5,1,1,1,1,1,1,0.746835,1,1,0.996835,0.743671,1,1,0.746835,1,1,1,1,1,1,1,1,1,1,0.496855,0.75,1,0.496835,0.5,1,0.996835,0.75,1,1,1,0.5,1,1,1,0.5,1,0.75,0.746835,0.996835,1,0.996855,1,0.75,1,0.996835,0.746835,1,1,1,1,1,0.993671,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,0.996855,0.496855,1]
area_under_roc_curve
0.921045298941167 [0.75,0.996855,1,1,1,0.75,1,1,1,0.75,1,0.996835,1,1,1,1,1,0.996835,1,0.75,0.75,0.75,0.993671,1,0.996835,0.743671,0.746835,0.5,1,1,1,1,1,0.746835,1,0.75,0.75,0.743671,1,1,1,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,0.996855,0.996835,0.993671,1,1,0.75,0.996855,1,1,0.993711,1,1,0.5,0.996855,1,0.746835,1,1,0.75,1,1,0.75,1,1,1,1,1,1,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,1,0.5,1,1,1,1,1,1,1,0.5,1]
area_under_roc_curve
0.9241700501552422 [0.996835,1,1,1,1,0.75,1,1,0.996855,1,1,1,1,1,1,0.75,1,1,0.493671,0.5,0.996835,1,1,1,0.996835,0.5,0.493711,1,1,1,0.996835,1,1,1,1,1,0.743671,0.746835,0.990506,1,1,1,1,1,0.5,0.75,0.5,0.996835,0.996855,1,1,1,1,1,0.75,0.75,1,1,0.5,1,0.993671,1,0.996855,0.996835,1,0.5,0.996855,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,1,1,0.75,1,1,0.75,1,1,0.5,1]
area_under_roc_curve
0.9148953108828912 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,0.75,1,0.996855,0.75,0.5,0.75,1,1,1,0.75,0.5,0.996855,1,0.5,1,1,1,0.996855,1,1,1,0.993671,0.993671,1,0.996855,0.5,1,1,1,1,1,1,0.75,0.996855,0.5,0.496855,0.75,0.75,0.996855,1,0.743671,1,1,1,0.990566,1,0.996835,1,1,1,0.496855,0.496855,1,1,0.746835,1,1,0.996835,1,0.996835,1,1,0.493711,1,1,0.75,1,0.75,1,0.75,0.75,1,0.996855,1,1,0.5,1,1,0.996855,1,1,1,1,0.75,1]
area_under_roc_curve
0.930598678449168 [0.996855,1,1,1,1,0.996855,1,1,0.75,1,1,1,1,1,1,1,1,0.496855,0.75,0.496855,1,1,0.5,1,1,0.493711,0.996855,0.996835,1,1,1,1,1,0.996855,0.996835,1,1,0.496835,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.993671,1,1,1,1,0.996835,1,1,0.743671,1,0.996835,0.996855,1,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1]
area_under_roc_curve
0.8991521375686649 [0.996855,1,1,1,1,1,1,1,1,1,0.996855,1,1,0.75,1,1,1,0.493711,0.5,0.490566,1,1,1,1,1,0.493711,0.5,0.996835,0.75,1,1,1,1,0.996855,0.75,1,0.746835,0.75,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,1,0.75,1,0.746835,0.75,0.996855,1,1,0.993671,0.75,0.75,0.75,0.496835,1,0.993711,0.993711,0.5,1,0.75,1,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,1,0.996835,1,0.746835,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.75,1,1]
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
f_measure
0.8699999999999998 [1,1,1,1,1,1,1,1,1,1,0.666667,1,1,0.666667,1,1,1,0.5,0.666667,0,1,1,0.666667,1,1,0.5,0,1,1,1,1,1,1,1,0.666667,0.666667,0.666667,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.666667,1,1,0.666667,1,0,1,1,0.666667,0.666667,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,0.666667,0.666667,1,1,0.5,1,1,0.8,1,1,0.666667,1,1,1,1,1,1,0.8,0.666667,0]
kappa
0.8041847611527833
kappa
0.8673195387774444
kappa
0.8610014215763703
kappa
0.8294579763925625
kappa
0.8420283559101142
kappa
0.8420533070088845
kappa
0.8483472216737096
kappa
0.8294445102451735
kappa
0.8609904430929627
kappa
0.7979000552617037
kb_relative_information_score
128.93234551626205
kb_relative_information_score
138.95416954327428
kb_relative_information_score
137.951987140573
kb_relative_information_score
132.94107512706694
kb_relative_information_score
134.94543993246936
kb_relative_information_score
134.94543993246936
kb_relative_information_score
135.94762233517056
kb_relative_information_score
132.94107512706694
kb_relative_information_score
137.951987140573
kb_relative_information_score
127.93016311356081
mean_absolute_error
0.0038750000000000013
mean_absolute_error
0.0026250000000000006
mean_absolute_error
0.0027500000000000007
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.0031250000000000006
mean_absolute_error
0.003000000000000001
mean_absolute_error
0.003375000000000001
mean_absolute_error
0.0027500000000000007
mean_absolute_error
0.004000000000000002
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
0.9041666666666666 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,1,0.666667,1,0]
predictive_accuracy
0.80625
predictive_accuracy
0.86875
predictive_accuracy
0.8625
predictive_accuracy
0.83125
predictive_accuracy
0.84375
predictive_accuracy
0.84375
predictive_accuracy
0.85
predictive_accuracy
0.83125
predictive_accuracy
0.8625
predictive_accuracy
0.8
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 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0.5,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.86875 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0]
recall
0.8625 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.83125 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5]
recall
0.84375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,0,1,1,0.5,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1]
recall
0.84375 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1]
recall
0.85 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1]
recall
0.83125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1]
recall
0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1]
recall
0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,0.5,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.19570707070707044
relative_absolute_error
0.13257575757575737
relative_absolute_error
0.1388888888888887
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.1578282828282826
relative_absolute_error
0.1515151515151513
relative_absolute_error
0.17045454545454522
relative_absolute_error
0.1388888888888887
relative_absolute_error
0.2020202020202018
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
0.051234753829797995
root_mean_squared_error
0.052440442408507586
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.05590169943749475
root_mean_squared_error
0.05477225575051662
root_mean_squared_error
0.058094750193111264
root_mean_squared_error
0.052440442408507586
root_mean_squared_error
0.0632455532033676
root_relative_squared_error
0.6256309946079566
root_relative_squared_error
0.5149286505444369
root_relative_squared_error
0.5270462766947296
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.5618332187193681
root_relative_squared_error
0.5504818825631801
root_relative_squared_error
0.5838742081211419
root_relative_squared_error
0.5270462766947296
root_relative_squared_error
0.6356417261637279
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
427.8048190000163
usercpu_time_millis
427.2934409999891
usercpu_time_millis
427.69632499999943
usercpu_time_millis
421.16390999999
usercpu_time_millis
418.56884999998556
usercpu_time_millis
427.04482400000643
usercpu_time_millis
422.8968209999948
usercpu_time_millis
421.6919940000139
usercpu_time_millis
417.3211070000207
usercpu_time_millis
418.84369299998525
usercpu_time_millis_testing
54.475192000012385
usercpu_time_millis_testing
53.98639500000968
usercpu_time_millis_testing
53.22764400000324
usercpu_time_millis_testing
53.52263300000004
usercpu_time_millis_testing
53.54534699998226
usercpu_time_millis_testing
57.42090200001826
usercpu_time_millis_testing
53.11616099999128
usercpu_time_millis_testing
52.76343900001734
usercpu_time_millis_testing
52.73782200001165
usercpu_time_millis_testing
53.59882999999854
usercpu_time_millis_training
373.32962700000394
usercpu_time_millis_training
373.30704599997944
usercpu_time_millis_training
374.4686809999962
usercpu_time_millis_training
367.64127699998994
usercpu_time_millis_training
365.0235030000033
usercpu_time_millis_training
369.62392199998817
usercpu_time_millis_training
369.78066000000354
usercpu_time_millis_training
368.92855499999655
usercpu_time_millis_training
364.583285000009
usercpu_time_millis_training
365.2448629999867