8840313
1
Jan van Rijn
9956
Supervised Classification
7707
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1)
6815363
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"median"
7660
strategy_nominal
"most_frequent"
7660
verbose
0
7660
categorical_features
[]
7661
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
7661
handle_unknown
"ignore"
7661
n_values
"auto"
7661
sparse
true
7661
threshold
0.0
7662
copy
true
7663
with_mean
false
7663
with_std
true
7663
C
1.2042258822932679
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.2988054219348222
7708
decision_function_shape
null
7708
degree
4
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gamma
0.07571286384067279
7708
kernel
"rbf"
7708
max_iter
-1
7708
probability
true
7708
random_state
42135
7708
shrinking
true
7708
tol
0.0017223817756778597
7708
verbose
false
7708
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1493
one-hundred-plants-texture
https://www.openml.org/data/download/1592285/phpoOxxNn
-1
18596300
description
https://api.openml.org/data/download/18596300/description.xml
-1
18596301
predictions
https://api.openml.org/data/download/18596301/predictions.arff
area_under_roc_curve
0.986785846432165 [0.992677,0.998658,0.998934,0.986734,0.947331,0.992814,0.967743,0.969007,0.999013,0.998697,0.999013,0.972165,0.999684,0.999961,0.990524,0.999882,0.989893,0.983733,0.982707,0.986418,0.999842,0.94133,0.981167,0.99392,0.947923,0.984681,0.920602,0.935052,0.999368,0.998737,0.989932,0.999921,0.936434,0.999842,0.985471,0.994354,0.998776,0.999724,0.997789,0.996289,1,0.947213,0.993328,0.983023,0.987326,0.999368,0.946936,0.999684,0.973981,0.997671,0.997236,0.997118,0.997907,0.994907,0.992775,0.945633,0.999961,0.994354,0.999882,0.997631,1,0.999447,0.994394,0.996447,0.999408,0.993051,0.999803,0.991985,0.999566,0.996131,0.998618,0.978719,0.999763,0.945396,0.998618,0.99925,0.996762,0.946265,0.998421,0.996841,0.999882,0.991748,0.958307,0.99771,0.947213,0.999684,0.975008,0.99925,0.999645,0.999763,0.9497,0.980693,0.997789,0.999566,0.973981,0.993801,0.998065,0.986418,0.999842,0.999329]
average_cost
0
f_measure
0.804060767374762 [0.866667,0.896552,0.903226,0.360656,0.714286,0.8,0.8,0.5625,0.896552,0.9375,0.875,0.333333,0.866667,0.896552,0.8,0.967742,0.705882,0.875,0.896552,0.375,0.903226,0.536585,0.933333,0.967742,0.409091,0.848485,0.857143,0.529412,0.933333,0.9375,0.933333,0.933333,0.709677,0.866667,0.857143,0.6,0.875,0.903226,0.827586,0.827586,0.933333,0.666667,0.705882,0.606061,0.551724,0.896552,0.540541,0.903226,0.827586,0.785714,0.774194,0.857143,0.875,0.866667,0.787879,0.571429,0.967742,0.727273,0.933333,0.814815,0.896552,0.896552,0.56,0.8,0.903226,0.8,0.903226,0.8125,0.967742,0.866667,0.848485,0.451613,0.857143,0.896552,0.903226,0.933333,0.83871,0.933333,0.866667,0.866667,0.896552,0.529412,0.529412,0.903226,0.774194,0.933333,0.6,0.875,0.967742,0.875,0.866667,0.8125,0.83871,0.903226,0.848485,0.827586,0.848485,0.903226,0.896552,0.866667]
kappa
0.7845875052148519
kb_relative_information_score
893.894484462557
mean_absolute_error
0.016045072004553036
mean_prior_absolute_error
0.019799992711748222
number_of_instances
1599 [15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16]
precision
0.8394431308226693 [0.866667,1,0.933333,0.244444,0.833333,0.857143,0.857143,0.5625,1,0.9375,0.875,0.357143,0.928571,1,0.857143,1,0.666667,0.875,1,0.234375,0.933333,0.44,1,1,0.321429,0.823529,1,0.5,1,0.9375,1,1,0.733333,0.928571,1,0.5,0.875,0.933333,0.923077,0.923077,1,0.647059,0.666667,0.588235,0.615385,1,0.47619,0.933333,0.923077,0.916667,0.8,1,0.875,0.928571,0.764706,0.666667,1,0.705882,1,1,1,1,0.411765,0.857143,0.933333,0.857143,0.933333,0.8125,1,0.928571,0.823529,0.466667,1,1,0.933333,1,0.866667,1,0.928571,0.928571,1,0.5,0.5,0.933333,0.8,1,0.642857,0.875,1,0.875,0.928571,0.8125,0.866667,0.933333,0.823529,0.923077,0.823529,0.933333,1,0.928571]
predictive_accuracy
0.7867417135709819
prior_entropy
6.6438309601245775
recall
0.7867417135709819 [0.866667,0.8125,0.875,0.6875,0.625,0.75,0.75,0.5625,0.8125,0.9375,0.875,0.3125,0.8125,0.8125,0.75,0.9375,0.75,0.875,0.8125,0.9375,0.875,0.6875,0.875,0.9375,0.5625,0.875,0.75,0.5625,0.875,0.9375,0.875,0.875,0.6875,0.8125,0.75,0.75,0.875,0.875,0.75,0.75,0.875,0.6875,0.75,0.625,0.5,0.8125,0.625,0.875,0.75,0.6875,0.75,0.75,0.875,0.8125,0.8125,0.5,0.9375,0.75,0.875,0.6875,0.8125,0.8125,0.875,0.75,0.875,0.75,0.875,0.8125,0.9375,0.8125,0.875,0.4375,0.75,0.8125,0.875,0.875,0.8125,0.875,0.8125,0.8125,0.8125,0.5625,0.5625,0.875,0.75,0.875,0.5625,0.875,0.9375,0.875,0.8125,0.8125,0.8125,0.875,0.875,0.75,0.875,0.875,0.8125,0.8125]
relative_absolute_error
0.81035747023446
root_mean_prior_squared_error
0.09949872432040595
root_mean_squared_error
0.08258946124898091
root_relative_squared_error
0.8300554787318296
total_cost
0
area_under_roc_curve
0.9934743451954462 [1,1,1,1,1,0.996835,0.943038,1,1,1,1,0.990506,1,1,0.987342,1,0.993671,1,1,0.996835,1,0.810127,1,0.974684,0.974684,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.927215,1,0.993671,0.996835,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,1,0.996835,0.996835,1,1,1,0.987342,0.996835,1,1,0.971519,0.996835,1,1,0.984177,0.981013,0.993711,1,1,1]
area_under_roc_curve
0.992190858609983 [1,0.993711,1,1,0.987342,0.984177,1,0.924528,0.996835,0.996835,1,0.962025,1,1,0.996835,1,0.981013,1,1,0.990506,1,0.939873,1,1,0.936709,0.987342,0.874214,0.996835,1,0.996835,1,1,1,1,1,0.987342,1,1,0.996835,0.996835,1,0.993711,0.962264,0.993671,1,1,0.974684,1,1,1,1,1,0.993711,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987421,1,0.993671,1,0.943396,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,0.754717,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.990506,1,1,1,1]
area_under_roc_curve
0.9971556305230476 [1,1,1,0.993671,1,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,0.96519,1,1,0.993711,1,1,0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,0.981132,1,1,1,0.996835,0.974684,1,0.987342,1,0.996835,1,1,0.996835,0.996835,1,0.987421,1,1,0.993711,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.977848,1,1,1,0.996835,1,1,1,0.993671,1,0.981013,1,1,0.987421,1,0.981013,1,1,1,1,0.996835,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.9955721081124113 [1,1,0.996835,0.996835,1,1,0.993671,0.952532,1,1,1,0.993711,1,1,0.990506,1,0.990506,1,0.981013,1,1,0.955696,1,1,0.987421,1,1,0.939873,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,0.981132,0.990506,0.990506,1,1,1,1,1,1,1,0.990506,0.984177,0.981013,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.990506,0.987342,1,1,1,1,0.987342,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.993711,1,1,1,1,0.987421]
area_under_roc_curve
0.9961281446540882 [1,1,1,1,1,1,1,0.96519,1,1,1,0.987421,1,1,0.930818,1,0.977848,1,0.990506,1,1,0.993711,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.990506,0.993671,0.984177,1,0.993671,1,1,1,0.987342,1,1,1,0.987342,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.996835,1,0.996835,0.987421,1,1,1,0.911392,1,1,1,1,1,1,0.993711,1,1,1,1,1,1]
area_under_roc_curve
0.9964533078576544 [1,1,1,0.974843,0.968553,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.805031,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,1,1,0.993671,0.981132,1,1,0.993671,1,1,0.990506,1,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.993711,0.984177,1,0.955696,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.996835,1]
area_under_roc_curve
0.9931287815460552 [1,1,1,1,1,0.977848,0.987421,0.981013,1,1,0.993671,0.952532,1,1,1,1,1,1,1,1,1,0.987421,0.981132,1,0.987342,1,1,0.899371,1,1,1,1,0.977848,1,0.958861,1,1,0.996835,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,0.860759,1,1,1,1,1,1,1,0.974843,0.962025,1,1,1,1,1,1,1,1,0.874214,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9877853574556166 [0.924528,1,0.993671,1,0.993711,0.990506,1,1,1,1,1,0.996835,1,1,1,1,0.993711,0.936709,1,0.889241,1,1,1,1,0.822785,1,0.927215,0.993711,1,1,1,1,0.708861,1,1,0.993671,0.996835,0.996835,1,1,1,0.987342,0.996835,0.987421,1,1,0.993711,1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,0.993711,0.993711,1,1,1,1,0.993711,0.974684,0.990506,0.990506,1,1,0.993671,1,0.981013,1,0.924528,1,1,1,1,1,0.996835,1,1,0.981013,1,1,1,1]
area_under_roc_curve
0.9971195565639676 [1,1,1,0.993671,1,1,1,1,0.996835,1,1,0.996835,1,1,0.993671,1,1,1,1,1,1,0.971519,1,1,0.996835,1,1,1,1,1,1,0.996835,0.968553,1,0.987421,1,1,1,1,1,1,0.993671,0.996835,0.893082,0.993711,1,1,1,0.958861,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,0.993711,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1]
area_under_roc_curve
0.9936741820932957 [1,1,1,0.984076,0.761146,1,0.980892,1,1,0.993631,1,0.996815,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.980892,1,1,0.993631,1,1,1,1,1,1,1,1,1,1,0.993631,1,1,0.93949,1,0.987342,1,1,0.936709,1,0.993631,1,0.996815,1,1,1,1,1,1,0.996815,1,1,1,1,1,1,1,0.996815,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993631,1,0.990446,1,1,1,1,1,1,1,0.993671,1,1,0.949045,1,1,1,1,1]
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
kappa
0.8104864181933038
kappa
0.7725747226280255
kappa
0.7347542924807579
kappa
0.7410287789664838
kappa
0.7852603323727944
kappa
0.8231206569804169
kappa
0.8041228970855383
kappa
0.753671245855045
kappa
0.8104265402843602
kappa
0.8092076316947322
kb_relative_information_score
93.95543741491046
kb_relative_information_score
86.42791673831712
kb_relative_information_score
88.15064481368145
kb_relative_information_score
84.6276818903979
kb_relative_information_score
90.30510262146616
kb_relative_information_score
94.31936460737025
kb_relative_information_score
89.62279804527205
kb_relative_information_score
84.192568343562
kb_relative_information_score
89.91242297278866
kb_relative_information_score
92.38054701479099
mean_absolute_error
0.015655397728398204
mean_absolute_error
0.01637627969935384
mean_absolute_error
0.01616267879326037
mean_absolute_error
0.016510509109563025
mean_absolute_error
0.015870589179556208
mean_absolute_error
0.01546643578492039
mean_absolute_error
0.016024712609975678
mean_absolute_error
0.016407863434654318
mean_absolute_error
0.01609747487556935
mean_absolute_error
0.01587773296125837
mean_prior_absolute_error
0.01980002942907592
mean_prior_absolute_error
0.01980002942907592
mean_prior_absolute_error
0.019800029429075917
mean_prior_absolute_error
0.019800029429075917
mean_prior_absolute_error
0.01980002942907591
mean_prior_absolute_error
0.01979995585638609
mean_prior_absolute_error
0.01979995585638609
mean_prior_absolute_error
0.01979995585638609
mean_prior_absolute_error
0.019799955856386102
mean_prior_absolute_error
0.01979995631910742
number_of_instances
160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,1,2,2,2,2,1,1,2,2,1,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,2,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1]
number_of_instances
160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,2,2,2,2,2,2,1,2,2,2,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,1,2,1,2,2,2,2,2,2,2,1,1,2,2,1,1,2,1]
number_of_instances
160 [2,1,2,2,2,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,1,1,2,2,2,1,2,1,1,1,1,2,1,1,1,2,2,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1]
number_of_instances
160 [2,1,2,2,2,1,2,2,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,1,1,1,2,2,2,2,2,1,1,1,1,2,1,1,1,2,1,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,1,1,2,2,2,1]
number_of_instances
160 [2,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,2,2,1,2,2,1,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,2,1,1,2,2,2,1,2,1,1,2,2,1,1,1,2,2,2,2]
number_of_instances
160 [1,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,1,2,1,2,2,2,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,1,2,1,2,2,2,1,2,1,1,2,2,2,1,1,2,2,2,2]
number_of_instances
160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,1,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,1,2,2,2,1,2]
number_of_instances
160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,1,1,1,2,2,1,2,1,1,1,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,2,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,2,2,2,2,1,2]
number_of_instances
160 [1,2,1,2,2,2,2,2,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,1,1,2,2,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,1,2,2,2,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2]
number_of_instances
159 [1,2,1,2,2,2,2,1,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,2,1,2,1,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,2,1,1,1,2]
predictive_accuracy
0.8125
predictive_accuracy
0.775
predictive_accuracy
0.7375
predictive_accuracy
0.74375
predictive_accuracy
0.7875
predictive_accuracy
0.825
predictive_accuracy
0.80625
predictive_accuracy
0.75625
predictive_accuracy
0.8125
predictive_accuracy
0.8113207547169812
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
prior_entropy
6.6438309601245775
recall
0.8125 [1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,1,0,0.5,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,1]
recall
0.775 [1,0,1,1,0.5,0,1,0,0.5,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0,1,0.5,1,1,1,1,1,1,0,1,1,0.5,0.5,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,0.5,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,1,1,0]
recall
0.7375 [1,1,1,0.5,1,1,0.5,1,1,1,1,0,1,0.5,1,1,1,1,0.5,1,0.5,0,1,1,1,0.5,1,0.5,1,1,1,0,1,0.5,1,1,1,1,1,0,0.5,1,1,0.5,0,0.5,1,0.5,0.5,0,1,0,0.5,1,1,0.5,1,0,0,1,0.5,1,1,1,1,0.5,0.5,0,0,1,1,0.5,1,1,0.5,0.5,1,1,1,0.5,1,0.5,1,1,0,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,1,1]
recall
0.74375 [1,0,0.5,1,1,0,0.5,0,1,1,1,0,0.5,0.5,0.5,1,0.5,1,0.5,1,1,0.5,1,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,1,0.5,0,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,0.5,0.5,0.5,0,1,1,1,1,1,0,1,1,1,0,1,1,1,1,0.5,0,1,1,1,0.5,0.5,1,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0]
recall
0.7875 [0.5,1,1,1,1,1,1,0,0,1,1,0,0,1,0,1,0,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0.5,1,1,1,0,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,0.5,1]
recall
0.825 [1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,0,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,0,1,1,0,1,1,0,1,0.5,0.5,1,1,0.5,1,0.5,1]
recall
0.80625 [1,1,1,0,0,0.5,1,0.5,1,1,0.5,0.5,1,0,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1]
recall
0.75625 [0,1,0.5,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0.5,0,1,1,1,0,1,0.5,0,0,1,1,1,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,0.5,0,0,1,0,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,0,1,1,1,1]
recall
0.8125 [1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,0,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,0.5,0,1,0,1,0.5,1,1,1,0.5,1,0.5,1,1,0.5,0.5,0.5,1,1,1,1,0,1,0,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1]
recall
0.8113207547169812 [1,0.5,1,0.5,0.5,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,0,1,0,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,1,1,0.5]
relative_absolute_error
0.7906754777550273
relative_absolute_error
0.8270836039923063
relative_absolute_error
0.8162956954763827
relative_absolute_error
0.8338628570580657
relative_absolute_error
0.8015437167103698
relative_absolute_error
0.7811348619715226
relative_absolute_error
0.8093307240787214
relative_absolute_error
0.828681818972959
relative_absolute_error
0.8130055941704241
relative_absolute_error
0.8019074742066974
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949890883178135
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.09949853911503082
root_mean_prior_squared_error
0.0994985414402977
root_mean_squared_error
0.08076803026735173
root_mean_squared_error
0.08392492096508512
root_mean_squared_error
0.08324286948514131
root_mean_squared_error
0.08474274328926158
root_mean_squared_error
0.08186532801028534
root_mean_squared_error
0.08022914240661659
root_mean_squared_error
0.08230859384150782
root_mean_squared_error
0.08428398425616036
root_mean_squared_error
0.08269768865578117
root_mean_squared_error
0.08170470061325082
root_relative_squared_error
0.8117478997071501
root_relative_squared_error
0.8434757923523913
root_relative_squared_error
0.8366209284352712
root_relative_squared_error
0.8516952023316416
root_relative_squared_error
0.8227761386679288
root_relative_squared_error
0.8063348780816092
root_relative_squared_error
0.8272341943267167
root_relative_squared_error
0.8470876558169279
root_relative_squared_error
0.8311447523885142
root_relative_squared_error
0.8211648073482187
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
3185.515450000821
usercpu_time_millis
2944.6028439997463
usercpu_time_millis
3114.7359770002367
usercpu_time_millis
2940.0636200007284
usercpu_time_millis
2943.1089860008797
usercpu_time_millis
2953.1565590004902
usercpu_time_millis
2955.902421999781
usercpu_time_millis
2949.3383369990624
usercpu_time_millis
2932.0318490008503
usercpu_time_millis
2971.5897850001056
usercpu_time_millis_testing
149.04952899996715
usercpu_time_millis_testing
147.89844599908974
usercpu_time_millis_testing
157.1253109996178
usercpu_time_millis_testing
146.36559700011276
usercpu_time_millis_testing
147.08510200034652
usercpu_time_millis_testing
148.66423800049233
usercpu_time_millis_testing
160.29180100122176
usercpu_time_millis_testing
146.2222619993554
usercpu_time_millis_testing
145.25549199970555
usercpu_time_millis_testing
156.04171700033476
usercpu_time_millis_training
3036.465921000854
usercpu_time_millis_training
2796.7043980006565
usercpu_time_millis_training
2957.610666000619
usercpu_time_millis_training
2793.6980230006156
usercpu_time_millis_training
2796.023884000533
usercpu_time_millis_training
2804.492320999998
usercpu_time_millis_training
2795.610620998559
usercpu_time_millis_training
2803.116074999707
usercpu_time_millis_training
2786.776357001145
usercpu_time_millis_training
2815.548067999771