8827251
1
Jan van Rijn
9954
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)
6802301
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
0.1280615471130018
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.4736950641496114
7708
decision_function_shape
null
7708
degree
5
7708
gamma
0.014834820393693651
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
63582
7708
shrinking
false
7708
tol
0.04818591995900326
7708
verbose
false
7708
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
18570312
description
https://api.openml.org/data/download/18570312/description.xml
-1
18570313
predictions
https://api.openml.org/data/download/18570313/predictions.arff
area_under_roc_curve
0.9959663036616155 [0.985243,0.999487,0.999961,0.999487,0.998382,0.9927,0.999724,0.99854,0.996528,0.999921,0.998658,0.999566,0.999092,0.95565,0.998461,0.999132,1,0.991201,0.985559,0.971551,0.997751,0.999448,0.994831,0.999132,0.998895,0.979561,0.988557,0.999211,0.998382,1,0.999527,0.999921,1,0.991517,0.997475,0.999171,0.994081,0.988123,0.999132,0.999645,0.998658,0.997001,1,0.998895,0.996133,1,0.998974,0.998698,0.997435,0.996843,0.99566,0.998698,0.998027,0.993095,0.995581,0.991122,1,0.999132,0.993963,0.996133,0.997475,0.999487,0.99779,0.998935,0.999092,0.992148,0.987176,0.999724,0.997633,0.993016,0.998106,0.998737,0.994476,0.997633,0.988873,0.999408,0.999448,0.991043,0.999566,0.999961,0.996922,0.996054,0.997633,0.996252,0.999211,0.999921,0.999842,0.999092,0.99929,0.999684,0.998816,0.999605,0.997554,0.996528,0.99491,0.996646,0.997001,0.998224,0.970407,0.996054]
average_cost
0
f_measure
0.8053625465363342 [0.594595,0.903226,0.967742,0.857143,0.896552,0.733333,0.9375,0.756757,0.774194,0.896552,0.848485,0.896552,0.882353,0.666667,0.896552,0.903226,0.933333,0.516129,0.4375,0.285714,0.774194,0.9375,0.764706,0.857143,0.882353,0.486486,0.484848,0.875,0.903226,1,0.903226,0.967742,0.9375,0.545455,0.83871,0.882353,0.5625,0.555556,0.909091,0.896552,0.787879,0.875,0.967742,0.823529,0.857143,0.888889,0.896552,0.8125,0.722222,0.8125,0.848485,0.758621,0.8125,0.75,0.774194,0.516129,0.896552,0.969697,0.666667,0.742857,0.8,0.882353,0.689655,0.903226,0.909091,0.666667,0.545455,0.9375,0.8,0.6875,0.823529,0.827586,0.709677,0.896552,0.717949,0.9375,0.896552,0.666667,1,1,0.875,0.8125,0.83871,0.5625,0.875,0.903226,0.967742,0.83871,0.875,0.967742,0.758621,0.827586,0.866667,0.8125,0.882353,0.8,0.8,0.83871,0.533333,0.875]
kappa
0.8011363636363636
kb_relative_information_score
971.5791763511145
mean_absolute_error
0.01590920175513641
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
0.815690277648347 [0.52381,0.933333,1,1,1,0.785714,0.9375,0.666667,0.8,1,0.823529,1,0.833333,0.714286,1,0.933333,1,0.533333,0.4375,0.333333,0.8,0.9375,0.722222,0.789474,0.833333,0.428571,0.470588,0.875,0.933333,1,0.933333,1,0.9375,0.529412,0.866667,0.833333,0.5625,0.5,0.882353,1,0.764706,0.875,1,0.777778,1,0.8,1,0.8125,0.65,0.8125,0.823529,0.846154,0.8125,0.75,0.8,0.533333,1,0.941176,0.565217,0.684211,0.857143,0.833333,0.769231,0.933333,0.882353,0.6,0.529412,0.9375,0.857143,0.6875,0.777778,0.923077,0.733333,1,0.608696,0.9375,1,0.565217,1,1,0.875,0.8125,0.866667,0.5625,0.875,0.933333,1,0.866667,0.875,1,0.846154,0.923077,0.928571,0.8125,0.833333,0.736842,0.736842,0.866667,0.571429,0.875]
predictive_accuracy
0.803125
prior_entropy
6.6438561897747395
recall
0.803125 [0.6875,0.875,0.9375,0.75,0.8125,0.6875,0.9375,0.875,0.75,0.8125,0.875,0.8125,0.9375,0.625,0.8125,0.875,0.875,0.5,0.4375,0.25,0.75,0.9375,0.8125,0.9375,0.9375,0.5625,0.5,0.875,0.875,1,0.875,0.9375,0.9375,0.5625,0.8125,0.9375,0.5625,0.625,0.9375,0.8125,0.8125,0.875,0.9375,0.875,0.75,1,0.8125,0.8125,0.8125,0.8125,0.875,0.6875,0.8125,0.75,0.75,0.5,0.8125,1,0.8125,0.8125,0.75,0.9375,0.625,0.875,0.9375,0.75,0.5625,0.9375,0.75,0.6875,0.875,0.75,0.6875,0.8125,0.875,0.9375,0.8125,0.8125,1,1,0.875,0.8125,0.8125,0.5625,0.875,0.875,0.9375,0.8125,0.875,0.9375,0.6875,0.75,0.8125,0.8125,0.9375,0.875,0.875,0.8125,0.5,0.875]
relative_absolute_error
0.8034950381382011
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08237399202209267
root_relative_squared_error
0.8278897697606369
total_cost
0
area_under_roc_curve
0.996327919751612 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,0.974843,1,0.996835,0.990506,0.974843,0.987421,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.984177,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,0.949686,0.996835,1,1,1,1,1,1,0.958861,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.987421,0.993671,0.943038,1]
area_under_roc_curve
0.9954549299418834 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.870253,1,1,1,0.977848,0.996835,0.974843,1,0.993711,0.993711,1,1,0.984177,0.971519,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,0.987421,1,1,1,0.987342,1,1,1,1,1,0.996835,1,1,0.987421,1,1,1,1,1,1,1,1,0.990506,1,1,0.977848,0.990506,1,0.987421,0.993671,1,1,1,1,1,1,1,1,0.974684,1,1,1,0.996835,1,0.981132]
area_under_roc_curve
0.9966418875885678 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.993671,1,1,0.996835,1,0.936709,1,1,0.990506,1,1,0.962025,0.990506,1,1,1,1,1,1,0.981013,1,1,0.987421,1,0.993711,0.996835,0.996835,0.974843,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.990506,1,1,1,0.987421,1,0.990506,1,1,1,0.993671,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,0.996835,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,0.987421,1]
area_under_roc_curve
0.9968013593662922 [0.996835,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.990506,0.937107,0.971519,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.984177,1,1,0.977848,0.993671,0.996835,0.981132,1,0.993711,1,1,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.990506,1,1,1]
area_under_roc_curve
0.9959736485948569 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.899371,1,1,1,0.996835,0.987421,0.987342,1,1,1,1,1,0.993671,0.990506,1,1,1,1,1,1,0.984177,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.993671,1,0.993671,0.996835,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.943038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.96519,0.993711,1,1,0.930818,1]
area_under_roc_curve
0.9962435813231426 [0.911392,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.96519,1,1,1,1,0.993671,0.984177,0.984177,1,1,1,1,1,1,1,1,0.993671,1,0.987342,0.996835,1,0.993671,1,1,0.993711,1,1,1,1,0.993671,0.996835,1,1,0.993671,1,0.996835,0.990506,1,1,0.996835,1,0.996835,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.993671]
area_under_roc_curve
0.9960913243372342 [0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,1,1,1,0.993671,0.977848,0.996835,1,0.993671,1,1,0.90566,0.962264,1,1,1,0.996835,1,1,1,1,1,0.977848,0.993671,1,1,1,1,1,1,0.974843,1,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.898734,1]
area_under_roc_curve
0.99712179563729 [0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.987421,0.993671,0.987342,0.996835,1,1,1,0.993671,0.987421,1,1,0.974843,1,1,1,1,1,1,1,0.984177,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,0.962264,1,0.996835,1,1,0.990506,1,1,0.996835,1,1,1,1,1,1,0.974843,0.924528,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.993671]
area_under_roc_curve
0.9971175662765701 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,1,0.968553,0.996835,1,0.968553,0.990506,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.977848,0.981013,0.987421,1,1,1,1,0.993671,1,1,1,1,1,0.993671,1,1,1,1,0.993711,0.974684,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,1,1,1]
area_under_roc_curve
0.9974723350051751 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.987421,0.962025,0.987421,0.996835,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.984177,1,1,1,0.993671,0.996835,0.993711,0.993711,0.984177,1,1,1,1,1,0.987342,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1]
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
0.7726285872182529
kappa
0.7915185974887466
kappa
0.8168252339031227
kappa
0.797860160290576
kappa
0.8167384177890121
kappa
0.8041615667074664
kappa
0.8104639684106614
kappa
0.8230787457546797
kappa
0.810493900272415
kappa
0.7663588286368301
kb_relative_information_score
98.47379568187208
kb_relative_information_score
95.60935863546318
kb_relative_information_score
96.04982292066622
kb_relative_information_score
95.32966650428399
kb_relative_information_score
96.93525354534955
kb_relative_information_score
96.74219470991902
kb_relative_information_score
101.01573332650815
kb_relative_information_score
97.13863112604338
kb_relative_information_score
97.1970360181967
kb_relative_information_score
97.08768388281058
mean_absolute_error
0.015688213969528565
mean_absolute_error
0.016140386309378772
mean_absolute_error
0.016111366384373015
mean_absolute_error
0.016220627332464738
mean_absolute_error
0.016067136010997598
mean_absolute_error
0.015913472427614647
mean_absolute_error
0.015349029387099436
mean_absolute_error
0.015973911455311882
mean_absolute_error
0.015864229902508103
mean_absolute_error
0.01576364437208755
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
0.775
predictive_accuracy
0.79375
predictive_accuracy
0.81875
predictive_accuracy
0.8
predictive_accuracy
0.81875
predictive_accuracy
0.80625
predictive_accuracy
0.8125
predictive_accuracy
0.825
predictive_accuracy
0.8125
predictive_accuracy
0.76875
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
0.775 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,0.5,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.79375 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0]
recall
0.81875 [0.5,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1]
recall
0.8 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,1,1,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1]
recall
0.81875 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.80625 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1]
recall
0.8125 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.825 [0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,0,1,1,1,1,1,1,1,1,0.5]
recall
0.8125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0,1,0.5,1]
recall
0.76875 [1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0.5,1,0,1,1,1,0,1,0,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.7923340388650777
relative_absolute_error
0.8151710257261991
relative_absolute_error
0.8137053729481306
relative_absolute_error
0.8192236026497329
relative_absolute_error
0.811471515706948
relative_absolute_error
0.803710728667405
relative_absolute_error
0.77520350439896
relative_absolute_error
0.8067632048137301
relative_absolute_error
0.801223732449903
relative_absolute_error
0.7961436551559354
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
0.08150935094977722
root_mean_squared_error
0.08315224590921115
root_mean_squared_error
0.08306730870965097
root_mean_squared_error
0.08365622793491201
root_mean_squared_error
0.08279227745431349
root_mean_squared_error
0.0824865183849516
root_mean_squared_error
0.08013992752199975
root_mean_squared_error
0.08232787831243635
root_mean_squared_error
0.08229903929595948
root_mean_squared_error
0.08225646938056626
root_relative_squared_error
0.8191998000176044
root_relative_squared_error
0.8357115156249029
root_relative_squared_error
0.8348578646501008
root_relative_squared_error
0.8407767255653059
root_relative_squared_error
0.8320936965301771
root_relative_squared_error
0.8290207022595054
root_relative_squared_error
0.8054365767174219
root_relative_squared_error
0.8274263095405723
root_relative_squared_error
0.8271364665194312
root_relative_squared_error
0.8267086227718053
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
1852.33530800906
usercpu_time_millis
1995.9158680139808
usercpu_time_millis
1881.6695519926725
usercpu_time_millis
1670.0644489901606
usercpu_time_millis
1787.7572769939434
usercpu_time_millis
1735.4552470060298
usercpu_time_millis
1799.5696760044666
usercpu_time_millis
1511.8891620077193
usercpu_time_millis
1792.3971339914715
usercpu_time_millis
1713.1720379984472
usercpu_time_millis_testing
197.0299960084958
usercpu_time_millis_testing
234.8838180041639
usercpu_time_millis_testing
141.96232399262954
usercpu_time_millis_testing
193.70093799079768
usercpu_time_millis_testing
187.67742499767337
usercpu_time_millis_testing
195.85248400107957
usercpu_time_millis_testing
196.14906700735446
usercpu_time_millis_testing
195.21003200497944
usercpu_time_millis_testing
194.72622498869896
usercpu_time_millis_testing
195.55332099844236
usercpu_time_millis_training
1655.3053120005643
usercpu_time_millis_training
1761.0320500098169
usercpu_time_millis_training
1739.707228000043
usercpu_time_millis_training
1476.363510999363
usercpu_time_millis_training
1600.07985199627
usercpu_time_millis_training
1539.6027630049502
usercpu_time_millis_training
1603.4206089971121
usercpu_time_millis_training
1316.6791300027398
usercpu_time_millis_training
1597.6709090027725
usercpu_time_millis_training
1517.6187170000048