8803186
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)
6778267
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
5.166929111578464
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.7177546519023601
7708
decision_function_shape
null
7708
degree
5
7708
gamma
0.14356724069100682
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
2501
7708
shrinking
true
7708
tol
2.7013316243476966e-05
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
18522377
description
https://api.openml.org/data/download/18522377/description.xml
-1
18522378
predictions
https://api.openml.org/data/download/18522378/predictions.arff
area_under_roc_curve
0.9952304292929293 [0.982757,0.999211,0.999961,0.999329,0.998343,0.991635,0.999645,0.997356,0.995147,0.999763,0.998224,0.999527,0.998935,0.943734,0.998027,0.998422,1,0.992661,0.983744,0.972932,0.99708,0.999092,0.993095,0.998895,0.998737,0.977588,0.988202,0.998619,0.998146,1,0.999092,0.999921,1,0.989978,0.995739,0.998461,0.993332,0.98398,0.998698,0.999408,0.998027,0.996094,1,0.998737,0.99562,1,0.998777,0.997909,0.996922,0.996725,0.995028,0.998461,0.997909,0.993963,0.994515,0.992069,1,0.998974,0.99199,0.995581,0.996528,0.999487,0.996843,0.998777,0.99858,0.992069,0.984336,0.999645,0.996607,0.991359,0.997475,0.998303,0.99416,0.996488,0.989228,0.999171,0.998816,0.990767,0.99925,0.999961,0.99637,0.995857,0.997317,0.994397,0.999053,0.999724,0.999803,0.998501,0.998974,0.999329,0.999605,0.999605,0.997159,0.994279,0.992858,0.995265,0.995344,0.997554,0.965278,0.9942]
average_cost
0
f_measure
0.7829861019649074 [0.631579,0.933333,0.967742,0.814815,0.857143,0.689655,0.9375,0.702703,0.75,0.857143,0.848485,0.896552,0.882353,0.606061,0.857143,0.777778,0.933333,0.580645,0.388889,0.242424,0.733333,0.9375,0.6875,0.857143,0.857143,0.444444,0.516129,0.848485,0.903226,0.967742,0.903226,0.967742,0.9375,0.484848,0.8,0.848485,0.413793,0.5,0.848485,0.896552,0.727273,0.875,0.967742,0.833333,0.857143,0.914286,0.814815,0.787879,0.705882,0.787879,0.875,0.8,0.787879,0.647059,0.774194,0.625,0.896552,0.9375,0.631579,0.647059,0.814815,0.833333,0.689655,0.903226,0.875,0.702703,0.4,0.9375,0.709677,0.709677,0.823529,0.785714,0.709677,0.896552,0.7,0.909091,0.896552,0.615385,0.967742,1,0.848485,0.8125,0.83871,0.529412,0.909091,0.866667,0.896552,0.8,0.903226,0.83871,0.774194,0.785714,0.866667,0.75,0.882353,0.777778,0.742857,0.8,0.555556,0.83871]
kappa
0.7771464646464646
kb_relative_information_score
943.944003261933
mean_absolute_error
0.016073423235357286
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
0.797366438722358 [0.545455,1,1,1,1,0.769231,0.9375,0.619048,0.75,1,0.823529,1,0.833333,0.588235,1,0.7,1,0.6,0.35,0.235294,0.785714,0.9375,0.6875,0.789474,0.789474,0.4,0.533333,0.823529,0.933333,1,0.933333,1,0.9375,0.470588,0.857143,0.823529,0.461538,0.5,0.823529,1,0.705882,0.875,1,0.75,1,0.842105,1,0.764706,0.666667,0.764706,0.875,0.857143,0.764706,0.611111,0.8,0.625,1,0.9375,0.545455,0.611111,1,0.75,0.769231,0.933333,0.875,0.619048,0.428571,0.9375,0.733333,0.733333,0.777778,0.916667,0.733333,1,0.583333,0.882353,1,0.521739,1,1,0.823529,0.8125,0.866667,0.5,0.882353,0.928571,1,0.857143,0.933333,0.866667,0.8,0.916667,0.928571,0.75,0.833333,0.7,0.684211,0.857143,0.5,0.866667]
predictive_accuracy
0.779375
prior_entropy
6.6438561897747395
recall
0.779375 [0.75,0.875,0.9375,0.6875,0.75,0.625,0.9375,0.8125,0.75,0.75,0.875,0.8125,0.9375,0.625,0.75,0.875,0.875,0.5625,0.4375,0.25,0.6875,0.9375,0.6875,0.9375,0.9375,0.5,0.5,0.875,0.875,0.9375,0.875,0.9375,0.9375,0.5,0.75,0.875,0.375,0.5,0.875,0.8125,0.75,0.875,0.9375,0.9375,0.75,1,0.6875,0.8125,0.75,0.8125,0.875,0.75,0.8125,0.6875,0.75,0.625,0.8125,0.9375,0.75,0.6875,0.6875,0.9375,0.625,0.875,0.875,0.8125,0.375,0.9375,0.6875,0.6875,0.875,0.6875,0.6875,0.8125,0.875,0.9375,0.8125,0.75,0.9375,1,0.875,0.8125,0.8125,0.5625,0.9375,0.8125,0.8125,0.75,0.875,0.8125,0.75,0.6875,0.8125,0.75,0.9375,0.875,0.8125,0.75,0.625,0.8125]
relative_absolute_error
0.8117890522907705
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08328210463861384
root_relative_squared_error
0.8370166449618146
total_cost
0
area_under_roc_curve
0.9962871188599636 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.977848,1,0.981132,1,1,0.981013,0.993711,0.981132,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.993671,0.993671,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,0.943396,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.949367,1]
area_under_roc_curve
0.9947431534113523 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.867089,1,1,1,0.977848,0.993671,0.987421,1,0.993711,0.981132,1,1,0.984177,0.968354,1,1,1,1,1,1,0.996835,0.977848,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,0.993671,1,1,0.993671,1,1,1,0.993711,1,1,1,0.987342,1,1,1,1,1,0.996835,1,1,0.981132,1,1,1,1,1,1,1,0.996835,0.996835,1,1,0.968354,0.990506,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.955696,1,1,1,0.996835,1,0.981132]
area_under_roc_curve
0.9960495283018869 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.990506,1,1,0.993671,1,0.936709,1,1,0.990506,1,1,0.946203,0.990506,0.993711,1,1,1,1,1,0.981013,1,1,0.987421,1,0.993711,0.996835,0.996835,0.962264,1,1,1,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,1,0.987421,0.996835,0.990506,1,1,0.993711,0.990506,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,0.996835,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.9964854012419391 [0.996835,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,0.993671,1,1,0.987342,0.937107,0.981013,1,1,0.993671,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.955975,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.984177,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.968354,0.987342,0.996835,0.993711,1,0.993711,1,1,0.993671,1,1,1,1,0.990506,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.984177,1,1,1]
area_under_roc_curve
0.9949882075471699 [1,1,1,1,1,1,1,0.996835,0.993711,1,0.987342,1,1,0.874214,1,0.996835,1,0.990506,0.968553,0.984177,1,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.984177,1,1,0.996835,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.984177,1,0.990506,1,1,0.987342,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.949367,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.958861,0.993711,1,1,0.899371,1]
area_under_roc_curve
0.9952942142345353 [0.901899,1,1,1,1,0.977848,1,0.990506,1,1,1,1,1,1,0.993711,1,1,1,1,0.96519,1,1,1,1,0.993671,0.981013,0.977848,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.987342,0.993671,1,0.990506,1,1,0.993711,1,1,1,1,0.993671,0.993671,1,1,0.993671,1,0.996835,0.987342,1,1,0.993671,0.993711,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.993671,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.993711,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.990506]
area_under_roc_curve
0.9947476315579967 [0.996835,1,1,1,1,1,1,0.993671,0.993711,1,1,1,0.993671,1,1,1,1,1,1,0.971519,0.993671,1,0.990506,1,1,0.886792,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.974843,1,0.996835,0.996835,0.993711,1,1,1,1,1,0.996835,1,1,0.996835,0.990506,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,0.993711,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835,1,0.838608,1]
area_under_roc_curve
0.9966488535944589 [0.996835,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,0.993711,1,1,0.993711,0.993671,0.984177,0.996835,1,0.993671,1,0.993671,0.993711,1,1,0.968553,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.981132,0.955975,1,1,1,1,0.987342,1,1,0.996835,1,1,1,1,1,1,0.974843,0.90566,1,1,0.996835,1,1,1,1,1,1,1,0.968553,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,0.993671,1,1,1,0.987342]
area_under_roc_curve
0.9965247094180396 [1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,0.993711,0.996835,0.981132,0.996835,1,0.955975,0.990506,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.955696,1,1,1,1,1,1,0.996835,1,1,0.993671,0.993711,1,0.993671,1,1,0.987342,0.971519,0.993711,1,1,1,1,0.990506,1,1,1,1,1,0.987342,1,1,1,1,0.993711,0.971519,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.984177,1,1,1,1]
area_under_roc_curve
0.9971160735610225 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,0.96519,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.990506,0.996835,0.993711,0.987421,0.977848,1,1,1,1,1,0.987342,0.987342,1,0.987342,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.993671,1,1,1,1,1,1,1,0.996835,0.993711,0.996835,1,1]
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
0.753700414446418
kappa
0.7599494630448516
kappa
0.7789095503178175
kappa
0.7725747226280255
kappa
0.8104415133085854
kappa
0.772592680326898
kappa
0.7915103652517275
kappa
0.8041306322315681
kappa
0.785226420308737
kappa
0.7411003236245954
kb_relative_information_score
95.88631906346545
kb_relative_information_score
92.7275907397015
kb_relative_information_score
92.72149404233558
kb_relative_information_score
91.98997571036449
kb_relative_information_score
94.27886340024784
kb_relative_information_score
94.07506966876298
kb_relative_information_score
98.91762911978537
kb_relative_information_score
94.91580851044687
kb_relative_information_score
94.1245734528413
kb_relative_information_score
94.30667955398171
mean_absolute_error
0.01586453683795319
mean_absolute_error
0.016336552716826803
mean_absolute_error
0.016337319149881936
mean_absolute_error
0.01643959846641077
mean_absolute_error
0.016203126029287574
mean_absolute_error
0.016058516756325247
mean_absolute_error
0.015444083264252072
mean_absolute_error
0.016079452868128695
mean_absolute_error
0.016040150982200248
mean_absolute_error
0.01593089528230616
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
0.75625
predictive_accuracy
0.7625
predictive_accuracy
0.78125
predictive_accuracy
0.775
predictive_accuracy
0.8125
predictive_accuracy
0.775
predictive_accuracy
0.79375
predictive_accuracy
0.80625
predictive_accuracy
0.7875
predictive_accuracy
0.74375
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
0.75625 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,0.5,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,0,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.7625 [1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,0,0,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,0.5,0]
recall
0.78125 [1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,0,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1]
recall
0.775 [0,1,1,0,0.5,0,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1]
recall
0.8125 [1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.775 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,0.5,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,0.5]
recall
0.79375 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0.5,0.5,0.5,0.5,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.80625 [0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5]
recall
0.7875 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,0,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,0,1,1,1,0.5,0,1,0.5,1]
recall
0.74375 [1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0.5,1,0,1,1,1,0,1,0,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.8012392342400588
relative_absolute_error
0.8250784200417565
relative_absolute_error
0.8251171287819146
relative_absolute_error
0.8302827508288254
relative_absolute_error
0.818339698448866
relative_absolute_error
0.811036199814405
relative_absolute_error
0.7800042052652549
relative_absolute_error
0.8120935791984175
relative_absolute_error
0.8101086354646576
relative_absolute_error
0.8045906708235421
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
0.08245078465995909
root_mean_squared_error
0.0841433866424341
root_mean_squared_error
0.08421470820285183
root_mean_squared_error
0.08475135288323209
root_mean_squared_error
0.083580900951143
root_mean_squared_error
0.08331945062812446
root_mean_squared_error
0.08079017054044406
root_mean_squared_error
0.08301240745163047
root_mean_squared_error
0.08332596636193804
root_mean_squared_error
0.08316677976597323
root_relative_squared_error
0.8286615648105298
root_relative_squared_error
0.845672854796231
root_relative_squared_error
0.846389663448862
root_relative_squared_error
0.8517831454202605
root_relative_squared_error
0.8400196608933331
root_relative_squared_error
0.8373919862788795
root_relative_squared_error
0.8119717649438701
root_relative_squared_error
0.8343060862459416
root_relative_squared_error
0.8374574718676474
root_relative_squared_error
0.8358575863813772
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
1899.6612090000085
usercpu_time_millis
1696.4800400000045
usercpu_time_millis
1682.8739190000022
usercpu_time_millis
1703.8600240000007
usercpu_time_millis
1686.9637989999974
usercpu_time_millis
2051.7652119999993
usercpu_time_millis
1740.8602060000007
usercpu_time_millis
1734.6667699999969
usercpu_time_millis
1670.588591000005
usercpu_time_millis
1681.9858069999966
usercpu_time_millis_testing
143.95241600000475
usercpu_time_millis_testing
140.8744770000041
usercpu_time_millis_testing
144.4663480000017
usercpu_time_millis_testing
138.64645600000358
usercpu_time_millis_testing
138.30147099999834
usercpu_time_millis_testing
188.22338700000074
usercpu_time_millis_testing
140.0721309999966
usercpu_time_millis_testing
139.29527299999478
usercpu_time_millis_testing
133.22775800000386
usercpu_time_millis_testing
139.12996499999508
usercpu_time_millis_training
1755.7087930000037
usercpu_time_millis_training
1555.6055630000003
usercpu_time_millis_training
1538.4075710000004
usercpu_time_millis_training
1565.2135679999972
usercpu_time_millis_training
1548.662327999999
usercpu_time_millis_training
1863.5418249999987
usercpu_time_millis_training
1600.788075000004
usercpu_time_millis_training
1595.3714970000021
usercpu_time_millis_training
1537.360833000001
usercpu_time_millis_training
1542.8558420000015