8958780
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)
6894384
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
2.5343496481925443
7650
cache_size
200
7650
class_weight
null
7650
coef0
0.31708077888603037
7650
decision_function_shape
"ovr"
7650
degree
3
7650
gamma
0.0015946483356092196
7650
kernel
"rbf"
7650
max_iter
-1
7650
probability
false
7650
random_state
1
7650
shrinking
false
7650
tol
0.000185491116598834
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
18832822
description
https://api.openml.org/data/download/18832822/description.xml
-1
18832823
predictions
https://api.openml.org/data/download/18832823/predictions.arff
area_under_roc_curve
0.8686868686868686 [0.747159,0.937184,1,1,0.9375,0.718434,0.999684,1,0.93529,0.96875,0.874684,0.998737,0.778725,0.780619,1,0.96875,1,0.587121,0.618687,0.559659,0.780619,0.96875,0.746528,1,0.843119,0.683712,0.590593,0.998422,0.968434,0.999053,0.999053,1,0.936237,0.713384,0.780934,0.715278,0.620581,0.588068,0.967487,0.999684,0.873422,0.968119,1,0.998422,0.905934,0.968119,0.874053,0.905303,0.968119,0.842803,0.780619,0.904987,0.874684,0.874053,0.873106,0.589331,1,0.968434,0.685922,0.748422,0.935606,0.936869,0.873106,0.968119,0.936869,0.716856,0.587753,1,0.936869,0.748737,0.779356,0.843434,0.809028,0.9375,0.780619,0.968434,0.968434,0.684343,0.96875,0.999684,0.75,0.936869,0.904987,0.839962,0.966856,0.937184,0.96875,0.905934,0.843119,0.999684,0.686553,0.968434,0.936553,0.937184,0.96875,0.905303,0.684343,0.997475,0.561869,0.873737]
average_cost
0
f_measure
0.7408910064271739 [0.484848,0.903226,1,1,0.933333,0.583333,0.969697,1,0.756757,0.967742,0.827586,0.888889,0.545455,0.666667,1,0.967742,1,0.15,0.2,0.148148,0.666667,0.967742,0.457143,1,0.758621,0.352941,0.206897,0.864865,0.9375,0.914286,0.914286,1,0.823529,0.35,0.692308,0.411765,0.235294,0.162162,0.857143,0.969697,0.727273,0.909091,1,0.864865,0.866667,0.909091,0.774194,0.8125,0.909091,0.733333,0.666667,0.787879,0.827586,0.774194,0.705882,0.181818,1,0.9375,0.444444,0.551724,0.777778,0.875,0.705882,0.909091,0.875,0.482759,0.157895,1,0.875,0.571429,0.580645,0.785714,0.540541,0.933333,0.666667,0.9375,0.9375,0.375,0.967742,0.969697,0.666667,0.875,0.787879,0.564103,0.810811,0.903226,0.967742,0.866667,0.758621,0.969697,0.48,0.9375,0.848485,0.903226,0.967742,0.8125,0.375,0.8,0.2,0.75]
kappa
0.7373737373737373
kb_relative_information_score
1183.0921204762903
mean_absolute_error
0.005199999999999944
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
0.7577157225803914 [0.470588,0.933333,1,1,1,0.875,0.941176,1,0.666667,1,0.923077,0.8,0.529412,0.818182,1,1,1,0.125,0.166667,0.181818,0.818182,1,0.421053,1,0.846154,0.333333,0.230769,0.761905,0.9375,0.842105,0.842105,1,0.777778,0.291667,0.9,0.388889,0.222222,0.142857,0.789474,0.941176,0.705882,0.882353,1,0.761905,0.928571,0.882353,0.8,0.8125,0.882353,0.785714,0.818182,0.764706,0.923077,0.8,0.666667,0.176471,1,0.9375,0.545455,0.615385,0.7,0.875,0.666667,0.882353,0.875,0.538462,0.136364,1,0.875,0.666667,0.6,0.916667,0.47619,1,0.818182,0.9375,0.9375,0.375,1,0.941176,1,0.875,0.764706,0.478261,0.714286,0.933333,1,0.928571,0.846154,0.941176,0.666667,0.9375,0.823529,0.933333,1,0.8125,0.375,0.666667,0.5,0.75]
predictive_accuracy
0.74
prior_entropy
6.6438561897747395
recall
0.74 [0.5,0.875,1,1,0.875,0.4375,1,1,0.875,0.9375,0.75,1,0.5625,0.5625,1,0.9375,1,0.1875,0.25,0.125,0.5625,0.9375,0.5,1,0.6875,0.375,0.1875,1,0.9375,1,1,1,0.875,0.4375,0.5625,0.4375,0.25,0.1875,0.9375,1,0.75,0.9375,1,1,0.8125,0.9375,0.75,0.8125,0.9375,0.6875,0.5625,0.8125,0.75,0.75,0.75,0.1875,1,0.9375,0.375,0.5,0.875,0.875,0.75,0.9375,0.875,0.4375,0.1875,1,0.875,0.5,0.5625,0.6875,0.625,0.875,0.5625,0.9375,0.9375,0.375,0.9375,1,0.5,0.875,0.8125,0.6875,0.9375,0.875,0.9375,0.8125,0.6875,1,0.375,0.9375,0.875,0.875,0.9375,0.8125,0.375,1,0.125,0.75]
relative_absolute_error
0.2626262626262594
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.0721110255092794
root_relative_squared_error
0.7247430753394742
total_cost
0
area_under_roc_curve
0.8488177692858849 [0.490566,0.75,1,1,0.75,1,1,1,1,1,0.75,0.996835,0.75,0.5,1,0.5,1,0.5,0.5,0.984277,0.496855,1,0.987421,1,0.5,0.5,0.5,0.996835,1,0.996855,1,1,0.496835,0.5,0.75,0.990566,0.996855,0.990566,1,1,0.996855,0.996855,1,1,1,0.996835,1,0.993711,1,0.75,1,1,1,1,0.996855,0.484277,1,1,0.5,1,1,0.996855,0.75,1,1,0.5,0.5,1,1,0.5,0.746835,1,1,0.75,0.496855,0.996835,0.75,0.5,1,1,0.75,0.996835,1,0.996855,1,0.75,0.75,1,0.5,1,0.75,1,1,0.996835,1,0.746835,0.493711,0.993671,0.496835,1]
area_under_roc_curve
0.8929026351405144 [1,1,1,1,1,1,0.996835,1,1,1,0.75,1,0.5,0.75,1,1,1,0.5,0.5,0.990566,1,1,0.993711,1,1,0.5,0.496835,1,1,0.996855,1,1,1,0.5,0.75,0.990566,0.996855,0.990566,1,1,1,1,1,0.996835,0.75,1,0.996855,1,1,1,1,0.75,0.996855,1,1,0.487421,1,1,1,0.75,0.996855,0.996855,1,1,1,0.5,0.5,1,1,1,0.743671,1,1,1,1,1,1,0.990506,1,1,0.5,1,0.993711,1,1,1,1,1,0.5,1,0.75,1,0.75,0.75,1,1,0.993711,0.996835,0.5,0.5]
area_under_roc_curve
0.8771395589523125 [0.746835,0.75,1,1,1,1,1,1,1,1,0.75,1,0.993711,1,1,1,1,0.5,0.996855,0.5,1,1,0.5,1,0.996855,0.75,0.75,0.996855,1,1,1,1,1,0.5,0.996855,0.996855,0.981132,0.984277,1,1,0.75,1,1,0.996855,0.996835,1,0.75,1,0.75,1,0.746835,1,1,0.75,1,0.5,1,1,0.993711,0.496835,0.996855,1,0.75,0.5,1,0.996835,0.5,1,1,0.5,0.746835,0.996835,0.987421,1,1,1,1,0.496835,0.75,0.996835,1,1,0.996835,0.5,0.996855,1,1,1,1,0.996855,0.5,1,1,1,1,1,0.5,0.996835,0.5,1]
area_under_roc_curve
0.8582915372979857 [0.493671,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,0.996855,0.75,1,1,1,0.5,0.987421,0.5,0.5,1,0.993671,1,0.5,0.5,0.5,0.996855,1,1,0.996855,1,1,0.5,0.5,0.987421,0.990566,0.477987,0.996855,1,1,0.996855,1,0.996855,1,1,0.75,1,1,1,0.5,1,1,0.75,1,0.5,1,1,0.993711,0.75,0.996855,1,0.996835,1,0.75,0.5,0.5,1,0.996855,0.746835,0.5,0.75,0.990566,1,1,1,1,0.75,1,1,1,1,0.746835,0.5,0.993711,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.5,0.996835,0.5,0.746835]
area_under_roc_curve
0.861396385638086 [0.746835,1,1,1,1,0.5,1,1,0.996855,1,1,1,0.993711,0.496855,1,1,1,0.5,0.968553,0.5,0.5,1,0.75,1,1,0.996835,0.5,1,0.996855,1,0.996855,1,0.996835,0.746835,1,1,0.496835,0.5,1,1,0.996835,1,1,1,1,0.996855,1,1,1,0.5,0.5,0.496855,0.75,0.996855,0.743671,0.5,1,0.996835,0.5,0.993711,1,1,0.996855,1,0.75,1,0.481132,1,0.75,0.75,0.75,0.75,0.996855,1,0.75,1,0.996835,0.5,1,1,0.5,1,0.75,0.993671,0.996855,1,1,1,1,1,1,0.75,0.996855,1,0.75,1,0.5,0.996855,0.5,1]
area_under_roc_curve
0.8770400445824376 [0.75,0.996855,1,1,1,0.5,1,1,0.996855,0.75,0.75,0.996835,0.993711,0.996855,1,1,1,0.5,0.984277,0.5,0.5,0.75,0.75,1,0.746835,0.990506,0.75,1,1,1,1,1,0.996835,0.996835,1,0.75,0.5,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,1,0.75,1,0.996855,0.75,1,0.993671,1,1,1,0.5,1,0.993671,1,0.993711,1,1,0.5,0.984277,1,1,0.75,0.75,0.75,0.996855,1,0.75,1,1,0.496835,1,1,0.5,0.75,1,0.993671,1,1,1,1,0.996835,1,0.493711,1,1,1,1,1,0.5,0.996855,0.5,1]
area_under_roc_curve
0.8738754876204124 [0.996835,1,1,1,1,0.5,1,1,0.993711,1,0.996855,0.996855,1,1,1,1,1,0.987421,0.5,0.5,0.996835,1,0.496835,1,0.75,0.496855,0.490566,1,1,1,0.996835,1,1,0.987421,0.75,0.5,0.493671,0.5,0.990506,1,1,0.75,1,1,0.5,0.75,0.5,0.746835,1,1,1,0.996835,1,1,0.746835,0.5,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.996855,0.990566,1,1,0.75,0.996855,1,0.75,1,0.75,1,1,1,1,1,0.75,1,1,0.996835,1,1,1,0.5,0.5,1,0.996855,1,0.746835,1,1,0.75,1,1,0.5,0.746835]
area_under_roc_curve
0.8646206512220364 [0.75,1,1,1,1,0.75,1,1,0.996855,1,1,1,0.996835,1,1,1,1,0.977987,0.5,0.5,0.75,1,0.75,1,0.75,0.990566,0.493711,0.996835,0.5,0.996835,1,1,1,0.987421,0.75,0.5,0.5,0.5,1,0.996855,0.5,1,1,1,0.5,1,0.996835,1,0.993711,0.5,0.496855,0.75,0.75,0.996855,1,0.5,1,1,1,0.993711,1,1,1,1,1,0.493711,0.984277,1,1,0.746835,1,1,0.5,1,0.746835,1,1,0.490566,1,1,0.5,1,0.75,1,0.746835,0.75,1,0.496855,1,1,0.5,1,1,1,1,1,0.75,1,0.496835,1]
area_under_roc_curve
0.8677852081840618 [0.496855,1,1,1,1,0.996855,1,1,0.75,1,1,0.996855,0.5,0.75,1,1,1,0.987421,0.5,0.496855,1,1,0.493711,1,1,0.490566,0.987421,0.996835,1,1,1,1,0.996855,0.987421,1,0.5,0.5,0.5,0.75,1,0.5,1,1,0.996835,1,1,0.996855,0.75,1,0.993711,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.5,0.5,0.75,0.75,1,0.996835,1,1,0.5,1,0.996835,0.996855,1,0.5,0.75,1,0.75,1,1,0.993711,1,1,1,1,1,0.990566,1,1,1,1,0.996855,1,0.5,1,1,1,1,0.746835,0.990566,0.996835,0.75,0.5]
area_under_roc_curve
0.8677454024361116 [1,1,1,1,1,0.5,1,1,0.996835,1,1,1,0.5,0.75,1,1,1,0.481132,0.5,0.5,1,1,1,1,1,0.496855,0.5,1,1,1,1,1,1,0.990566,0.5,0.5,0.5,0.5,1,1,0.993711,1,1,1,1,1,1,1,1,0.996855,0.75,1,1,0.746835,1,0.984277,1,1,0.746835,0.5,0.75,0.75,0.496835,1,0.993711,0.993711,0.5,1,0.75,0.996855,0.996855,0.5,0.493671,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.490566,0.996835,0.996835,1,0.75,1,1,0.75,0.996835,1,0.75,1,1,0.990566,1,0.75,0.993711]
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
0.6970055627884957
kappa
0.785345065698615
kappa
0.7538073068728793
kappa
0.7159763313609467
kappa
0.722244141087351
kappa
0.7537490134175217
kappa
0.7474149498776541
kappa
0.7285567742444566
kappa
0.7348694074015625
kappa
0.7348589465377787
kb_relative_information_score
111.8952446703412
kb_relative_information_score
125.92579830815838
kb_relative_information_score
120.91488629465225
kb_relative_information_score
114.90179187844495
kb_relative_information_score
115.90397428114613
kb_relative_information_score
120.91488629465222
kb_relative_information_score
119.91270389195101
kb_relative_information_score
116.90615668384734
kb_relative_information_score
117.90833908654861
kb_relative_information_score
117.90833908654857
mean_absolute_error
0.006000000000000004
mean_absolute_error
0.004250000000000002
mean_absolute_error
0.004875000000000003
mean_absolute_error
0.005625000000000003
mean_absolute_error
0.005500000000000003
mean_absolute_error
0.004875000000000003
mean_absolute_error
0.005000000000000003
mean_absolute_error
0.005375000000000003
mean_absolute_error
0.005250000000000003
mean_absolute_error
0.005250000000000003
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
0.7
predictive_accuracy
0.7875
predictive_accuracy
0.75625
predictive_accuracy
0.71875
predictive_accuracy
0.725
predictive_accuracy
0.75625
predictive_accuracy
0.75
predictive_accuracy
0.73125
predictive_accuracy
0.7375
predictive_accuracy
0.7375
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
0.7 [0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,0,1,0,0,1,0,1,1,1,0,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,0,0,1,1,0,0.5,1,1,0.5,0,1,0.5,0,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0.5,1,1,1,1,0.5,0,1,0,1]
recall
0.7875 [1,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0.5,1,0.5,0.5,1,1,1,1,0,0]
recall
0.75625 [0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0.5,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1]
recall
0.71875 [0,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0,1,0,0,1,1,1,0,0,0,1,1,1,1,1,1,0,0,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,0.5,0,0,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,0,0.5]
recall
0.725 [0.5,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,0,1,0,0,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0,1,1,0,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0,1,0,1]
recall
0.75625 [0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0,0,0.5,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,0,1,1,0,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1]
recall
0.75 [1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,0,1,0.5,0,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,0.5,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,0.5,1,1,0,0.5]
recall
0.73125 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,1,0.5,1,0.5,1,0,1,0,1,1,1,1,1,0.5,0,0,0,1,1,0,1,1,1,0,1,1,1,1,0,0,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,0,1,0.5,1,1,0,1,1,0,1,0.5,1,0.5,0.5,1,0,1,1,0,1,1,1,1,1,0.5,1,0,1]
recall
0.7375 [0,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,0,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,0.5,0,0,1,0,0,0,0.5,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,0]
recall
0.7375 [1,1,1,1,1,0,1,1,1,1,1,1,0,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,0,0.5,0.5,0,1,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1]
relative_absolute_error
0.3030303030303027
relative_absolute_error
0.2146464646464644
relative_absolute_error
0.24621212121212094
relative_absolute_error
0.2840909090909088
relative_absolute_error
0.27777777777777746
relative_absolute_error
0.24621212121212094
relative_absolute_error
0.2525252525252522
relative_absolute_error
0.2714646464646462
relative_absolute_error
0.26515151515151486
relative_absolute_error
0.26515151515151486
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
0.07745966692414837
root_mean_squared_error
0.06519202405202651
root_mean_squared_error
0.06982120021884472
root_mean_squared_error
0.07500000000000002
root_mean_squared_error
0.07416198487095665
root_mean_squared_error
0.06982120021884472
root_mean_squared_error
0.07071067811865477
root_mean_squared_error
0.07331439149307592
root_mean_squared_error
0.07245688373094722
root_mean_squared_error
0.07245688373094722
root_relative_squared_error
0.7784989441615228
root_relative_squared_error
0.6552044942557468
root_relative_squared_error
0.7017294652672368
root_relative_squared_error
0.7537783614444089
root_relative_squared_error
0.7453559924999297
root_relative_squared_error
0.7017294652672368
root_relative_squared_error
0.7106690545187011
root_relative_squared_error
0.7368373585325954
root_relative_squared_error
0.7282190812544189
root_relative_squared_error
0.7282190812544189
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
473.27386300003127
usercpu_time_millis
471.35919700008344
usercpu_time_millis
477.7904570000828
usercpu_time_millis
474.5512619999772
usercpu_time_millis
485.5129610000404
usercpu_time_millis
469.55574100002195
usercpu_time_millis
486.3180659999671
usercpu_time_millis
475.9406589998889
usercpu_time_millis
468.8606529998651
usercpu_time_millis
462.1308240000417
usercpu_time_millis_testing
55.43266299991956
usercpu_time_millis_testing
55.865190000076836
usercpu_time_millis_testing
55.642792000071495
usercpu_time_millis_testing
55.984289999969405
usercpu_time_millis_testing
55.245560000003024
usercpu_time_millis_testing
54.88343000001805
usercpu_time_millis_testing
53.58770099996946
usercpu_time_millis_testing
54.15766799978883
usercpu_time_millis_testing
54.59023699995669
usercpu_time_millis_testing
54.41973100005271
usercpu_time_millis_training
417.8412000001117
usercpu_time_millis_training
415.4940070000066
usercpu_time_millis_training
422.14766500001133
usercpu_time_millis_training
418.5669720000078
usercpu_time_millis_training
430.26740100003735
usercpu_time_millis_training
414.6723110000039
usercpu_time_millis_training
432.7303649999976
usercpu_time_millis_training
421.7829910001001
usercpu_time_millis_training
414.27041599990844
usercpu_time_millis_training
407.711092999989