8698181
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)
6675600
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
12281.596146982398
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.5772885804771086
7708
decision_function_shape
null
7708
degree
5
7708
gamma
0.16032044625359804
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
5946
7708
shrinking
false
7708
tol
0.0007995704775210946
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
18313299
description
https://api.openml.org/data/download/18313299/description.xml
-1
18313300
predictions
https://api.openml.org/data/download/18313300/predictions.arff
area_under_roc_curve
0.9954288983585857 [0.984257,0.99925,0.999961,0.999408,0.998382,0.992069,0.999684,0.997593,0.995857,0.999842,0.998343,0.999645,0.998816,0.944721,0.998343,0.99858,1,0.992385,0.982915,0.972735,0.997001,0.999211,0.993529,0.998974,0.998501,0.978812,0.988991,0.99858,0.997988,0.999961,0.999132,0.999921,1,0.990017,0.99637,0.998303,0.99345,0.984691,0.998935,0.999408,0.99858,0.996607,1,0.99854,0.99562,1,0.998816,0.99779,0.997199,0.996765,0.99491,0.998461,0.997869,0.993726,0.995107,0.9927,1,0.999014,0.992385,0.995975,0.996883,0.999487,0.996922,0.998895,0.99854,0.992464,0.985638,0.999684,0.99712,0.993056,0.996922,0.99854,0.994081,0.997396,0.98907,0.999211,0.999211,0.990609,0.99929,0.999921,0.996212,0.996133,0.997356,0.994752,0.999053,0.999803,0.999803,0.998816,0.998974,0.999487,0.999605,0.999605,0.997356,0.995344,0.993253,0.995818,0.996094,0.998422,0.965002,0.994437]
average_cost
0
f_measure
0.7896507611516705 [0.648649,0.866667,0.967742,0.814815,0.857143,0.774194,0.9375,0.736842,0.774194,0.857143,0.848485,0.896552,0.857143,0.625,0.857143,0.823529,0.933333,0.606061,0.4,0.242424,0.774194,0.9375,0.6875,0.857143,0.857143,0.444444,0.545455,0.848485,0.903226,1,0.903226,0.967742,0.9375,0.5,0.83871,0.848485,0.516129,0.482759,0.875,0.896552,0.727273,0.875,0.933333,0.823529,0.857143,0.888889,0.857143,0.75,0.705882,0.75,0.848485,0.8,0.787879,0.6875,0.774194,0.580645,0.896552,0.9375,0.65,0.666667,0.785714,0.857143,0.689655,0.903226,0.909091,0.702703,0.387097,0.903226,0.8,0.6875,0.787879,0.785714,0.6875,0.896552,0.682927,0.9375,0.896552,0.648649,0.967742,1,0.848485,0.8125,0.83871,0.5625,0.909091,0.866667,0.967742,0.8,0.875,0.903226,0.8125,0.785714,0.866667,0.774194,0.882353,0.8,0.777778,0.8,0.555556,0.866667]
kappa
0.7840909090909091
kb_relative_information_score
951.9155383487158
mean_absolute_error
0.016023197358708795
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
0.8029811953440436 [0.571429,0.928571,1,1,1,0.8,0.9375,0.636364,0.8,1,0.823529,1,0.789474,0.625,1,0.777778,1,0.588235,0.368421,0.235294,0.8,0.9375,0.6875,0.789474,0.789474,0.4,0.529412,0.823529,0.933333,1,0.933333,1,0.9375,0.5,0.866667,0.823529,0.533333,0.538462,0.875,1,0.705882,0.875,1,0.777778,1,0.8,1,0.75,0.666667,0.75,0.823529,0.857143,0.764706,0.6875,0.8,0.6,1,0.9375,0.541667,0.6,0.916667,0.789474,0.769231,0.933333,0.882353,0.619048,0.4,0.933333,0.857143,0.6875,0.764706,0.916667,0.6875,1,0.56,0.9375,1,0.571429,1,1,0.823529,0.8125,0.866667,0.5625,0.882353,0.928571,1,0.857143,0.875,0.933333,0.8125,0.916667,0.928571,0.8,0.833333,0.736842,0.7,0.857143,0.5,0.928571]
predictive_accuracy
0.78625
prior_entropy
6.6438561897747395
recall
0.78625 [0.75,0.8125,0.9375,0.6875,0.75,0.75,0.9375,0.875,0.75,0.75,0.875,0.8125,0.9375,0.625,0.75,0.875,0.875,0.625,0.4375,0.25,0.75,0.9375,0.6875,0.9375,0.9375,0.5,0.5625,0.875,0.875,1,0.875,0.9375,0.9375,0.5,0.8125,0.875,0.5,0.4375,0.875,0.8125,0.75,0.875,0.875,0.875,0.75,1,0.75,0.75,0.75,0.75,0.875,0.75,0.8125,0.6875,0.75,0.5625,0.8125,0.9375,0.8125,0.75,0.6875,0.9375,0.625,0.875,0.9375,0.8125,0.375,0.875,0.75,0.6875,0.8125,0.6875,0.6875,0.8125,0.875,0.9375,0.8125,0.75,0.9375,1,0.875,0.8125,0.8125,0.5625,0.9375,0.8125,0.9375,0.75,0.875,0.875,0.8125,0.6875,0.8125,0.75,0.9375,0.875,0.875,0.75,0.625,0.8125]
relative_absolute_error
0.809252391853978
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08301148040653694
root_relative_squared_error
0.834296769092187
total_cost
0
area_under_roc_curve
0.9962473131120134 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.977848,1,0.981132,1,1,0.981013,0.993711,0.981132,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.968354,1,1,0.949686,0.996835,1,1,1,1,1,1,0.949367,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,1,0.993671,0.949367,1]
area_under_roc_curve
0.9949409382214791 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.867089,1,1,1,0.977848,0.993671,0.981132,1,0.993711,0.981132,1,1,0.987342,0.968354,1,1,1,1,1,1,0.996835,0.981013,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,0.993671,1,1,0.996835,1,1,1,0.993711,1,1,1,0.990506,1,1,1,1,1,0.996835,1,1,0.987421,1,1,1,1,1,1,1,0.996835,0.993671,1,1,0.971519,0.990506,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.958861,1,1,1,0.996835,1,0.981132]
area_under_roc_curve
0.9962470643260886 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.993671,1,1,0.993671,1,0.939873,1,1,0.990506,1,1,0.949367,0.990506,0.993711,1,1,1,1,1,0.981013,1,1,0.987421,1,0.993711,0.996835,0.996835,0.968553,1,1,1,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,1,1,0.987421,0.996835,0.990506,1,1,0.993711,0.990506,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,0.996835,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.996682937266141 [1,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.987342,0.943396,0.981013,1,1,0.993671,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.974684,0.987342,0.996835,0.987421,1,0.993711,1,1,0.993671,1,1,1,1,0.987342,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.984177,1,1,1]
area_under_roc_curve
0.9951461866093464 [1,1,1,1,1,1,1,0.996835,0.993711,1,0.987342,1,1,0.880503,1,0.996835,1,0.993671,0.968553,0.984177,1,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.984177,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.996835,0.987342,1,0.990506,0.996835,1,0.993671,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.943038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.962025,0.993711,1,1,0.899371,1]
area_under_roc_curve
0.9956895350688638 [0.901899,1,1,1,1,0.981013,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,1,0.968354,1,1,1,1,0.993671,0.981013,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.987342,0.996835,1,0.990506,1,1,0.993711,1,1,1,1,0.993671,0.993671,1,1,0.993671,1,0.996835,0.987342,1,1,0.996835,0.993711,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671]
area_under_roc_curve
0.9949048642623992 [0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,0.974684,0.993671,1,0.990506,1,1,0.90566,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,0.996835,0.990506,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,0.993711,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.835443,1]
area_under_roc_curve
0.9970426817132394 [1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,0.993711,1,1,0.993711,0.993671,0.984177,0.996835,1,0.996835,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.981132,0.962264,1,1,1,1,0.990506,1,1,0.996835,1,1,1,1,1,1,0.981132,0.918239,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.996835,1,0.993671,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.990506]
area_under_roc_curve
0.9966438778759651 [1,0.996835,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,0.993711,0.996835,0.968553,0.996835,1,0.955975,0.990506,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.987342,0.971519,0.993711,1,1,1,1,0.990506,1,1,1,1,1,0.990506,1,1,1,1,0.993711,0.974684,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.987342,1,1,1,1]
area_under_roc_curve
0.9971553817371233 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,0.962025,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.987342,0.996835,0.993711,0.987421,0.981013,1,1,1,1,1,0.987342,0.990506,1,0.987342,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.993671,1,1,0.996835,1,1,1,1,0.996835,0.993711,1,1,1]
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
0.7599873677561977
kappa
0.759939984996249
kappa
0.7978681405448086
kappa
0.7725747226280255
kappa
0.8041228970855383
kappa
0.7915432902996564
kappa
0.7978202495656295
kappa
0.7914856646394439
kappa
0.8041615667074664
kappa
0.7600442023837714
kb_relative_information_score
96.93476728737855
kb_relative_information_score
93.74969022309492
kb_relative_information_score
94.0208697310089
kb_relative_information_score
92.95591018708241
kb_relative_information_score
94.67850892356806
kb_relative_information_score
94.76248426854232
kb_relative_information_score
99.35472723419102
kb_relative_information_score
95.4762630743754
kb_relative_information_score
94.92405969689175
kb_relative_information_score
95.05825772258355
mean_absolute_error
0.01576337979758927
mean_absolute_error
0.016262245891023923
mean_absolute_error
0.016223016754358114
mean_absolute_error
0.016364603789236064
mean_absolute_error
0.016203778114534335
mean_absolute_error
0.01602415887694483
mean_absolute_error
0.015432387522349971
mean_absolute_error
0.016057485411584294
mean_absolute_error
0.015994362313346156
mean_absolute_error
0.015906555116120755
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
0.7625
predictive_accuracy
0.7625
predictive_accuracy
0.8
predictive_accuracy
0.775
predictive_accuracy
0.80625
predictive_accuracy
0.79375
predictive_accuracy
0.8
predictive_accuracy
0.79375
predictive_accuracy
0.80625
predictive_accuracy
0.7625
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
0.7625 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,0.5,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.7625 [1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,0.5,1,0,1,1,1,0,0,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,0.5,0]
recall
0.8 [1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,0,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1]
recall
0.775 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,0.5,1,0.5,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1]
recall
0.80625 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.79375 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5]
recall
0.8 [1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,0,0.5,0.5,0.5,0.5,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.79375 [0.5,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5]
recall
0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,0.5,1]
recall
0.7625 [1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0.5,1,0,1,1,1,0,1,0,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.7961302928075376
relative_absolute_error
0.821325550051712
relative_absolute_error
0.8193442805231358
relative_absolute_error
0.8264951408705069
relative_absolute_error
0.8183726320471872
relative_absolute_error
0.8093009533810506
relative_absolute_error
0.7794135112297953
relative_absolute_error
0.810984111696175
relative_absolute_error
0.8077960764316227
relative_absolute_error
0.8033613695010469
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
0.08199088444593543
root_mean_squared_error
0.08378248196993877
root_mean_squared_error
0.08371056254340895
root_mean_squared_error
0.08442390278406772
root_mean_squared_error
0.08352681264815848
root_mean_squared_error
0.08310760139243158
root_mean_squared_error
0.08060930319458637
root_mean_squared_error
0.0828449454656835
root_mean_squared_error
0.0830767572823802
root_mean_squared_error
0.08298012738784424
root_relative_squared_error
0.824039393747134
root_relative_squared_error
0.8420456263606155
root_relative_squared_error
0.841322808927473
root_relative_squared_error
0.8484921480975549
root_relative_squared_error
0.8394760529947066
root_relative_squared_error
0.8352628213488283
root_relative_squared_error
0.8101539797225444
root_relative_squared_error
0.8326230299609907
root_relative_squared_error
0.8349528263790319
root_relative_squared_error
0.8339816593981003
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
1669.3313829998715
usercpu_time_millis
1531.533174999936
usercpu_time_millis
1485.0214419999475
usercpu_time_millis
1486.5533860000824
usercpu_time_millis
1533.0970140000773
usercpu_time_millis
1480.81256699993
usercpu_time_millis
1544.8098659999232
usercpu_time_millis
1514.8433549999254
usercpu_time_millis
1478.1672369999797
usercpu_time_millis
1513.2924299999786
usercpu_time_millis_testing
141.59021899990876
usercpu_time_millis_testing
152.3081599999614
usercpu_time_millis_testing
138.6547019999398
usercpu_time_millis_testing
145.8457130000852
usercpu_time_millis_testing
139.2929419999973
usercpu_time_millis_testing
146.31318700003249
usercpu_time_millis_testing
179.06734999996843
usercpu_time_millis_testing
139.8970539999027
usercpu_time_millis_testing
139.2351150000195
usercpu_time_millis_testing
152.7864969999655
usercpu_time_millis_training
1527.7411639999627
usercpu_time_millis_training
1379.2250149999745
usercpu_time_millis_training
1346.3667400000077
usercpu_time_millis_training
1340.7076729999972
usercpu_time_millis_training
1393.80407200008
usercpu_time_millis_training
1334.4993799998974
usercpu_time_millis_training
1365.7425159999548
usercpu_time_millis_training
1374.9463010000227
usercpu_time_millis_training
1338.9321219999601
usercpu_time_millis_training
1360.505933000013