8822007
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)
6797057
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"mean"
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
100.96042593806108
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.6212776532352724
7708
decision_function_shape
null
7708
degree
2
7708
gamma
0.40523663474210453
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
19708
7708
shrinking
true
7708
tol
0.002082747574074004
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
18559868
description
https://api.openml.org/data/download/18559868/description.xml
-1
18559870
predictions
https://api.openml.org/data/download/18559870/predictions.arff
area_under_roc_curve
0.9965194917929294 [0.987847,1,1,0.999921,0.998501,0.994989,0.999803,0.999605,0.997514,0.999961,0.998895,0.999527,0.999487,0.975892,0.999329,0.999527,1,0.991675,0.984375,0.975339,0.998461,0.999566,0.996449,0.999329,0.998619,0.977036,0.992109,0.99929,0.998698,1,0.999724,1,1,0.990017,0.999053,0.998816,0.99349,0.984296,0.999724,0.999882,0.999211,0.998698,1,0.998935,0.997001,0.999921,0.999369,0.99929,0.997554,0.996646,0.994673,0.999132,0.998067,0.992345,0.996883,0.994042,1,0.999448,0.993371,0.996765,0.998816,0.999527,0.997633,0.999171,0.999369,0.994318,0.986111,0.999961,0.998027,0.993963,0.996646,0.999487,0.996094,0.998343,0.989347,0.999527,0.999448,0.991083,0.999961,1,0.995739,0.997317,0.997948,0.995265,0.999329,0.999921,0.999961,0.99925,0.999329,1,0.99925,0.999605,0.998816,0.997593,0.996843,0.997869,0.998224,0.998224,0.968592,0.997948]
average_cost
0
f_measure
0.8283829966477344 [0.648649,0.967742,0.967742,0.967742,0.933333,0.8,1,0.903226,0.83871,0.933333,0.882353,0.875,0.967742,0.714286,0.967742,0.903226,1,0.645161,0.4,0.258065,0.827586,0.967742,0.787879,1,0.875,0.388889,0.545455,0.848485,0.933333,0.933333,0.967742,0.967742,0.967742,0.551724,0.875,0.875,0.5625,0.516129,0.941176,0.896552,0.756757,0.909091,1,0.848485,0.9375,0.941176,0.857143,0.903226,0.764706,0.83871,0.787879,0.785714,0.8125,0.685714,0.689655,0.571429,1,0.9375,0.702703,0.742857,0.896552,0.823529,0.733333,0.903226,0.909091,0.682927,0.424242,0.969697,0.83871,0.727273,0.823529,0.933333,0.709677,0.967742,0.756757,0.909091,0.903226,0.685714,1,1,0.774194,0.848485,0.866667,0.533333,0.875,0.967742,0.933333,0.903226,0.933333,1,0.8125,0.903226,0.903226,0.875,0.967742,0.8,0.8,0.848485,0.516129,0.903226]
kappa
0.8238636363636365
kb_relative_information_score
987.4846452116352
mean_absolute_error
0.01598940095097364
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
0.8374369419967718 [0.571429,1,1,1,1,0.857143,1,0.933333,0.866667,1,0.833333,0.875,1,0.833333,1,0.933333,1,0.666667,0.368421,0.266667,0.923077,1,0.764706,1,0.875,0.35,0.529412,0.823529,1,1,1,1,1,0.615385,0.875,0.875,0.5625,0.533333,0.888889,1,0.666667,0.882353,1,0.823529,0.9375,0.888889,1,0.933333,0.722222,0.866667,0.764706,0.916667,0.8125,0.631579,0.769231,0.526316,1,0.9375,0.619048,0.684211,1,0.777778,0.785714,0.933333,0.882353,0.56,0.411765,0.941176,0.866667,0.705882,0.777778,1,0.733333,1,0.666667,0.882353,0.933333,0.631579,1,1,0.8,0.823529,0.928571,0.571429,0.875,1,1,0.933333,1,1,0.8125,0.933333,0.933333,0.875,1,0.736842,0.736842,0.823529,0.533333,0.933333]
predictive_accuracy
0.825625
prior_entropy
6.6438561897747395
recall
0.825625 [0.75,0.9375,0.9375,0.9375,0.875,0.75,1,0.875,0.8125,0.875,0.9375,0.875,0.9375,0.625,0.9375,0.875,1,0.625,0.4375,0.25,0.75,0.9375,0.8125,1,0.875,0.4375,0.5625,0.875,0.875,0.875,0.9375,0.9375,0.9375,0.5,0.875,0.875,0.5625,0.5,1,0.8125,0.875,0.9375,1,0.875,0.9375,1,0.75,0.875,0.8125,0.8125,0.8125,0.6875,0.8125,0.75,0.625,0.625,1,0.9375,0.8125,0.8125,0.8125,0.875,0.6875,0.875,0.9375,0.875,0.4375,1,0.8125,0.75,0.875,0.875,0.6875,0.9375,0.875,0.9375,0.875,0.75,1,1,0.75,0.875,0.8125,0.5,0.875,0.9375,0.875,0.875,0.875,1,0.8125,0.875,0.875,0.875,0.9375,0.875,0.875,0.875,0.5,0.875]
relative_absolute_error
0.8075455025744247
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.082246122932365
root_relative_squared_error
0.8266046370548463
total_cost
0
area_under_roc_curve
0.9965232167024919 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,1,0.981132,1,0.990506,0.984177,0.987421,1,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,1,0.962264,0.996835,1,1,1,1,1,1,0.958861,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.987421,0.993671,0.936709,1]
area_under_roc_curve
0.9964040482445664 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.892405,1,1,1,0.984177,0.996835,0.987421,1,0.993711,0.993711,1,1,0.981013,0.971519,1,1,1,1,1,1,0.996835,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.984177,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.974843]
area_under_roc_curve
0.997114580845474 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,0.939873,1,1,0.993671,1,1,0.946203,1,1,1,1,1,1,1,0.984177,1,1,0.993711,1,1,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,1,0.987421,1,0.993671,1,1,1,0.987342,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.9971966802006209 [1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.990506,0.943396,0.971519,1,1,0.996835,1,0.993711,0.990506,1,1,1,1,1,1,1,0.987342,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.984177,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,0.977848,0.984177,1,0.981132,1,0.993711,1,1,0.996835,1,1,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1]
area_under_roc_curve
0.9962097464373855 [1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,0.918239,1,1,1,0.990506,0.987421,0.990506,1,1,1,1,1,0.993671,0.990506,1,1,1,1,1,1,0.977848,1,1,0.993671,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.990506,0.993671,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.943038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.971519,0.993711,1,1,0.943396,1]
area_under_roc_curve
0.996401311599395 [0.920886,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,1,1,1,0.993671,0.990506,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.981013,0.996835,1,0.996835,1,1,0.993711,1,1,1,1,0.990506,0.996835,1,1,1,1,1,0.990506,1,1,1,1,0.996835,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1]
area_under_roc_curve
0.9954191047687285 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,0.968354,0.996835,1,0.990506,1,1,0.886792,0.974843,0.993671,1,1,0.996835,1,1,0.993711,1,1,0.971519,0.993671,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.987342,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.879747,1]
area_under_roc_curve
0.9973581422657432 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,0.990506,0.993671,0.996835,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.993671,1,1,1,1,1,1,1,1,1,0.993671,1,0.987421,0.962264,1,1,1,1,0.987342,1,1,0.993671,1,1,1,1,1,1,0.974843,0.924528,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835]
area_under_roc_curve
0.9972354908048722 [1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.993711,0.990506,0.974843,0.996835,1,0.981132,0.993671,1,0.993711,0.993711,1,1,1,1,1,1,0.993711,1,1,1,0.949367,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.993671,1,1,0.968354,0.990506,0.993711,1,1,1,1,0.996835,1,1,1,1,1,0.984177,1,1,1,1,0.993711,0.984177,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1]
area_under_roc_curve
0.9979465209776294 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.974684,0.987421,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,1,0.993711,0.993711,0.987342,1,1,1,1,1,0.990506,0.993671,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,1,1,0.996835,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1]
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
0.7978840991631139
kappa
0.8041306322315681
kappa
0.8862873613140126
kappa
0.8167963043392427
kappa
0.8104639684106614
kappa
0.8104789355233545
kappa
0.8167963043392427
kappa
0.829397361977727
kappa
0.86101788605046
kappa
0.8042156785347754
kb_relative_information_score
99.27015798054921
kb_relative_information_score
97.31326375738969
kb_relative_information_score
98.61704285342583
kb_relative_information_score
97.65466671206897
kb_relative_information_score
98.20652834633488
kb_relative_information_score
97.89161415705898
kb_relative_information_score
101.4630590086461
kb_relative_information_score
98.46131294915652
kb_relative_information_score
99.54318125068492
kb_relative_information_score
99.063818196321
mean_absolute_error
0.01585401026969977
mean_absolute_error
0.016180679521380033
mean_absolute_error
0.016081312683333775
mean_absolute_error
0.016179295614184335
mean_absolute_error
0.01613901358324094
mean_absolute_error
0.0160399197114303
mean_absolute_error
0.015615275152466537
mean_absolute_error
0.01605691953028753
mean_absolute_error
0.0158548861575848
mean_absolute_error
0.015892697286128153
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
0.8
predictive_accuracy
0.80625
predictive_accuracy
0.8875
predictive_accuracy
0.81875
predictive_accuracy
0.8125
predictive_accuracy
0.8125
predictive_accuracy
0.81875
predictive_accuracy
0.83125
predictive_accuracy
0.8625
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
0.8 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1]
recall
0.80625 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0]
recall
0.8875 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
recall
0.81875 [0,1,1,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1]
recall
0.8125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,0.5,0,1,1,1,1,1,0,1,1,1,0.5,1,1,0,0,0.5,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.8125 [0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1]
recall
0.81875 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.83125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5]
recall
0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1]
recall
0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.800707589378775
relative_absolute_error
0.8172060364333337
relative_absolute_error
0.8121875092592802
relative_absolute_error
0.8171361421305205
relative_absolute_error
0.8151016961232784
relative_absolute_error
0.810096955122741
relative_absolute_error
0.7886502602255814
relative_absolute_error
0.8109555318327021
relative_absolute_error
0.8007518261406453
relative_absolute_error
0.8026614790973801
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
0.0817345815562507
root_mean_squared_error
0.083002188176649
root_mean_squared_error
0.08243410820885724
root_mean_squared_error
0.08302773909384205
root_mean_squared_error
0.08274771299751546
root_mean_squared_error
0.08263635250742676
root_mean_squared_error
0.08057091214850998
root_mean_squared_error
0.08235821931904566
root_mean_squared_error
0.08177885576900638
root_mean_squared_error
0.0821405774739815
root_relative_squared_error
0.8214634527842004
root_relative_squared_error
0.8342033786679326
root_relative_squared_error
0.8284939601707131
root_relative_squared_error
0.8344601750478684
root_relative_squared_error
0.8316458068871918
root_relative_squared_error
0.8305265918505427
root_relative_squared_error
0.8097681351918032
root_relative_squared_error
0.8277312481305263
root_relative_squared_error
0.8219084253648035
root_relative_squared_error
0.8255438652858035
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
1613.250608996168
usercpu_time_millis
1484.151386001031
usercpu_time_millis
1469.8956880019978
usercpu_time_millis
1479.9038669989386
usercpu_time_millis
1488.370927003416
usercpu_time_millis
1534.3791450031858
usercpu_time_millis
1509.4643669981451
usercpu_time_millis
1508.931525000662
usercpu_time_millis
1493.5414469982788
usercpu_time_millis
1497.737485999096
usercpu_time_millis_testing
154.3751829995017
usercpu_time_millis_testing
157.7925179990416
usercpu_time_millis_testing
151.53443700182834
usercpu_time_millis_testing
151.80838699961896
usercpu_time_millis_testing
158.33522400134825
usercpu_time_millis_testing
180.82855800093967
usercpu_time_millis_testing
153.9169909992779
usercpu_time_millis_testing
153.71242000037455
usercpu_time_millis_testing
151.57906799868215
usercpu_time_millis_testing
153.53087399853393
usercpu_time_millis_training
1458.8754259966663
usercpu_time_millis_training
1326.3588680019893
usercpu_time_millis_training
1318.3612510001694
usercpu_time_millis_training
1328.0954799993197
usercpu_time_millis_training
1330.0357030020677
usercpu_time_millis_training
1353.5505870022462
usercpu_time_millis_training
1355.5473759988672
usercpu_time_millis_training
1355.2191050002875
usercpu_time_millis_training
1341.9623789995967
usercpu_time_millis_training
1344.206612000562