8829489
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)
6804539
axis
0
7660
categorical_features
[]
7660
copy
true
7660
fill_empty
0
7660
missing_values
"NaN"
7660
strategy
"most_frequent"
7660
strategy_nominal
"most_frequent"
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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
0.2581540964807176
7708
cache_size
200
7708
class_weight
null
7708
coef0
0.22170319036687847
7708
decision_function_shape
null
7708
degree
3
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gamma
0.04822835526322003
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
41775
7708
shrinking
true
7708
tol
0.012533004565159336
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
18574766
description
https://api.openml.org/data/download/18574766/description.xml
-1
18574767
predictions
https://api.openml.org/data/download/18574767/predictions.arff
area_under_roc_curve
0.9918161993306829 [0.998359,0.99846,0.998816,0.997078,0.954161,0.997592,0.99621,0.994078,0.994551,0.999882,0.999013,0.985668,0.9985,0.999803,0.990919,0.999013,0.993999,0.999842,0.999447,0.96419,0.999882,0.95424,0.992104,0.988748,0.938566,0.997592,0.966124,0.97335,0.999842,0.998697,0.990643,0.999882,0.948594,0.999052,0.999763,0.990603,0.998381,0.999842,0.99771,0.998816,0.999131,0.979706,0.994867,0.9771,0.984089,0.999329,0.979075,0.999842,0.964229,0.998618,0.998065,0.999171,0.996684,0.990169,0.989893,0.997315,0.999921,0.983536,0.998737,1,0.999961,0.997986,0.998776,0.999566,0.999368,0.998934,0.999329,0.99388,0.996684,0.989103,0.994473,0.983457,0.999763,0.966914,0.998658,0.999329,0.996526,0.999763,0.998539,0.992577,0.996447,0.982628,0.977219,0.996723,0.99313,0.999842,0.973073,0.998816,0.999842,0.999289,0.968178,0.995539,0.995104,1,0.995973,0.995539,0.996091,0.995578,0.999131,0.998816]
average_cost
0
f_measure
0.8272776946345726 [0.787879,0.933333,0.875,0.8,0.645161,0.787879,0.740741,0.727273,0.866667,0.888889,0.909091,0.628571,0.875,0.9375,0.75,0.967742,0.648649,0.903226,0.896552,0.857143,0.969697,0.594595,0.903226,0.967742,0.454545,0.875,0.83871,0.606061,0.896552,0.9375,0.882353,0.967742,0.666667,0.903226,0.857143,0.742857,0.823529,0.969697,0.8,0.857143,0.933333,0.758621,0.666667,0.512821,0.666667,0.903226,0.578947,0.903226,0.896552,0.875,0.733333,0.933333,0.823529,0.903226,0.742857,0.823529,0.933333,0.75,0.903226,0.896552,0.896552,0.909091,0.740741,0.857143,0.882353,0.827586,0.8125,0.777778,0.8125,0.875,0.848485,0.645161,0.933333,0.903226,0.933333,0.933333,0.787879,0.967742,0.848485,0.684211,0.933333,0.6875,0.588235,0.733333,0.848485,0.896552,0.56,0.903226,0.967742,0.882353,0.933333,0.709677,0.903226,0.933333,0.866667,0.823529,0.875,0.875,0.967742,0.882353]
kappa
0.8205950850079112
kb_relative_information_score
946.4467401850598
mean_absolute_error
0.015917137886533463
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
0.8433243972167924 [0.722222,1,0.875,0.857143,0.666667,0.764706,0.909091,0.705882,0.928571,0.8,0.882353,0.578947,0.875,0.9375,0.75,1,0.571429,0.933333,1,1,0.941176,0.52381,0.933333,1,0.357143,0.875,0.866667,0.588235,1,0.9375,0.833333,1,0.6,0.933333,1,0.684211,0.777778,0.941176,0.736842,1,1,0.846154,0.647059,0.434783,0.647059,0.933333,0.5,0.933333,1,0.875,0.785714,1,0.777778,0.933333,0.684211,0.777778,1,0.625,0.933333,1,1,0.882353,0.909091,1,0.833333,0.923077,0.8125,0.7,0.8125,0.875,0.823529,0.666667,1,0.933333,1,1,0.764706,1,0.823529,0.590909,1,0.6875,0.555556,0.785714,0.823529,1,0.777778,0.933333,1,0.833333,1,0.733333,0.933333,1,0.928571,0.777778,0.875,0.875,1,0.833333]
predictive_accuracy
0.8223889931207005
prior_entropy
6.6438309601245775
recall
0.8223889931207005 [0.866667,0.875,0.875,0.75,0.625,0.8125,0.625,0.75,0.8125,1,0.9375,0.6875,0.875,0.9375,0.75,0.9375,0.75,0.875,0.8125,0.75,1,0.6875,0.875,0.9375,0.625,0.875,0.8125,0.625,0.8125,0.9375,0.9375,0.9375,0.75,0.875,0.75,0.8125,0.875,1,0.875,0.75,0.875,0.6875,0.6875,0.625,0.6875,0.875,0.6875,0.875,0.8125,0.875,0.6875,0.875,0.875,0.875,0.8125,0.875,0.875,0.9375,0.875,0.8125,0.8125,0.9375,0.625,0.75,0.9375,0.75,0.8125,0.875,0.8125,0.875,0.875,0.625,0.875,0.875,0.875,0.875,0.8125,0.9375,0.875,0.8125,0.875,0.6875,0.625,0.6875,0.875,0.8125,0.4375,0.875,0.9375,0.9375,0.875,0.6875,0.875,0.875,0.8125,0.875,0.875,0.875,0.9375,0.9375]
relative_absolute_error
0.8038961487641919
root_mean_prior_squared_error
0.09949872432040595
root_mean_squared_error
0.08253446007161899
root_relative_squared_error
0.8295026959927787
total_cost
0
area_under_roc_curve
0.9938699148156992 [1,1,1,0.996835,1,0.984177,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,0.797468,1,0.96519,0.968354,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,0.993671,0.993671,1,1,0.993711,1,0.993711,1,1,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.993711,1,0.971519,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,0.993671,0.993711,1,0.977848,1,1,1,0.981013,1,1,1,0.990506,0.990506,1,1,1,1]
area_under_roc_curve
0.9925369198312239 [1,1,1,0.996835,0.981013,1,1,1,1,1,1,0.984177,1,1,0.993671,1,1,1,1,0.977848,1,0.933544,1,1,0.920886,0.993671,0.918239,0.987342,1,1,1,1,1,1,1,0.952532,1,1,1,1,1,1,0.962264,0.996835,1,1,0.96519,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,0.984177,1,1,1,1,1,0.924528,1,0.949367,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.996835,0.867925,1,1,1,0.977848,0.996835,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1]
area_under_roc_curve
0.9960452989411672 [1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,0.996835,1,1,0.993671,0.996835,1,1,1,1,0.987421,1,1,1,1,0.993711,1,1,1,1,1,1,0.984177,1,0.936709,1,1,1,0.996835,1,1,1,0.990506,1,0.911392,1,1,0.993711,1,0.920886,1,1,1,1,1,1,1,1,1,1,0.996835,1,1]
area_under_roc_curve
0.9956927692858846 [1,1,0.996835,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.955696,1,1,0.974843,1,1,0.943038,1,1,1,1,0.996835,1,1,0.987421,0.993711,1,0.974843,1,1,0.993711,0.981132,0.993671,1,1,1,1,1,1,1,0.996835,0.993671,0.949367,0.968354,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,0.977848,1,1,0.993671,1,0.984177,0.987421,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1]
area_under_roc_curve
0.9948677951596208 [1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.993711,0.987421,1,0.867925,1,0.968354,1,1,1,1,0.987421,0.971519,1,0.987421,0.996835,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,0.993711,0.996835,1,0.990506,0.993671,0.984177,1,0.993671,1,1,0.993711,0.996835,1,1,1,0.971519,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,0.993671,0.993671,1,0.993711,1,1,1,0.981013,0.996835,0.981132,0.993711,1,1,0.943038,1,1,1,0.981132,1,1,1,1,1,1,1,1,0.996835]
area_under_roc_curve
0.9953081462463178 [1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.742138,1,1,0.930818,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,0.981013,1,1,1,1,1,1,1,1,1,0.984177,0.993671,1,0.936709,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.981132,1,1,1,1,1,1,1,1,1,1,0.984177,0.974843,1,1,1,1,1,0.993711,1,1,0.987342,1,1,1,0.971519,1,1,1]
area_under_roc_curve
0.9951402157471539 [1,1,1,1,1,1,1,1,1,1,0.993671,0.946203,1,1,1,1,1,1,1,1,1,1,0.962264,1,0.962025,1,1,0.974843,0.993711,1,1,1,1,1,1,0.987342,0.993671,1,0.993671,1,1,1,0.996835,1,0.949367,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993671,1,1,1,1,0.996835,1,1,1,1,0.990506,0.987421,1,1,1,0.908228,1,1,1,1,1,1,1,0.993711,0.977848,0.987421,1,1,0.993711,1,1,1,1,0.968553,1,1,0.993711,1,1,1,1,1]
area_under_roc_curve
0.9866785088766816 [0.955975,1,1,1,0.981132,1,1,1,0.981013,1,0.996835,1,1,1,1,1,0.987421,1,1,0.952532,1,0.993711,1,1,0.740506,0.993711,0.838608,1,1,1,1,1,0.664557,1,1,0.990506,1,1,1,1,1,0.987342,1,0.955975,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,1,1,0.996835,1,1,1,1,1,1,0.990506,1,1,1,1,1,0.993711,1,1,1,0.987421,1,1,0.993671,0.962025,1,1,0.993671,0.993711,0.974684,1,0.937107,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,1]
area_under_roc_curve
0.9962498009712604 [1,1,1,0.993671,0.993671,1,1,1,1,1,1,0.987342,1,1,0.996835,0.993671,0.993711,1,1,1,1,0.996835,1,1,0.987342,1,1,1,1,1,1,0.996835,0.993711,0.993711,1,1,0.993671,1,1,1,1,1,1,0.880503,1,1,1,1,0.905063,1,1,1,1,0.996835,0.996835,0.993711,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.968553,1,0.996835]
area_under_roc_curve
0.9943582324625255 [1,0.993631,1,0.984076,0.792994,1,0.993631,1,1,1,1,0.996815,0.996815,1,1,0.996815,1,1,1,0.996815,1,0.996815,1,1,0.952229,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977707,0.990446,0.917722,1,1,0.93038,1,1,1,1,1,1,1,0.993671,1,1,0.993631,1,1,1,1,0.996815,1,1,0.996815,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993631,0.990446,1,1,1,1,1,1,1,1,1,1,1,1,0.993631,1,1,1,1]
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
0.8547062539481995
kappa
0.797876120168963
kappa
0.7978521794061908
kappa
0.7978202495656295
kappa
0.8230857323381905
kappa
0.8420657796027955
kappa
0.8610069101678184
kappa
0.7915268290756899
kappa
0.7915679772619612
kappa
0.8473783146022478
kb_relative_information_score
93.52132542539532
kb_relative_information_score
94.09635303895115
kb_relative_information_score
96.56367481386745
kb_relative_information_score
96.14694623243811
kb_relative_information_score
93.66912516821445
kb_relative_information_score
96.44361185820597
kb_relative_information_score
95.76537459763718
kb_relative_information_score
90.41293337045278
kb_relative_information_score
93.6750852296591
kb_relative_information_score
96.15231045024024
mean_absolute_error
0.01613614236622925
mean_absolute_error
0.016063466233733028
mean_absolute_error
0.0158234713594784
mean_absolute_error
0.015623181771948772
mean_absolute_error
0.015852532948483884
mean_absolute_error
0.015873193212027428
mean_absolute_error
0.01569920945200548
mean_absolute_error
0.016293444325168275
mean_absolute_error
0.016150833210530076
mean_absolute_error
0.015654261005221642
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
0.85625
predictive_accuracy
0.8
predictive_accuracy
0.8
predictive_accuracy
0.8
predictive_accuracy
0.825
predictive_accuracy
0.84375
predictive_accuracy
0.8625
predictive_accuracy
0.79375
predictive_accuracy
0.79375
predictive_accuracy
0.8490566037735848
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
0.85625 [1,1,0,1,0.5,1,0,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1]
recall
0.8 [1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0.5,1,0.5,1,1,0,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1]
recall
0.8 [1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,0,0,1,1,1,1,1,1,1,1,0,1,0,0,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,0.5,0,1,0.5,0,1,0,1,1,1,1,1,1,1,1]
recall
0.8 [1,0,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,1,0.5,1,0.5,0,1,1,0,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,0,1,1,0.5,1,1]
recall
0.825 [0.5,1,1,1,1,1,1,0,0,1,1,0,0,1,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,0.5,1]
recall
0.84375 [1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,1,0,1,1,1,1,0.5,1,1,1,0,1,1,1]
recall
0.8625 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,0,1,1,1,0.5,1,1,1,0,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1]
recall
0.79375 [0,1,1,1,0,0.5,1,1,0,1,1,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,0.5,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,0,1,1,0.5,0,1,0,1,0,0.5,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1]
recall
0.79375 [1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,0.5,1,0.5,0,0,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,0,1,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0,1,0.5]
recall
0.8490566037735849 [1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,0.5,1,1,1,1,1,0,0,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,1,1,1,1,0.5,1,0,0.5,1,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
relative_absolute_error
0.8149554738809464
relative_absolute_error
0.8112849675942486
relative_absolute_error
0.7991640323646172
relative_absolute_error
0.7890484116658163
relative_absolute_error
0.8006317872035476
relative_absolute_error
0.8016782121717628
relative_absolute_error
0.7928911339942207
relative_absolute_error
0.8229030631860293
relative_absolute_error
0.8157004655806306
relative_absolute_error
0.7906209868814161
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
0.08316992163618234
root_mean_squared_error
0.08277138029346359
root_mean_squared_error
0.08176364811033615
root_mean_squared_error
0.08167695776933753
root_mean_squared_error
0.08279897646919979
root_mean_squared_error
0.08199335324092676
root_mean_squared_error
0.08177280557994783
root_mean_squared_error
0.08418967037520053
root_mean_squared_error
0.08356849336824018
root_mean_squared_error
0.08158846662031524
root_relative_squared_error
0.8358877761845033
root_relative_squared_error
0.8318822916279585
root_relative_squared_error
0.8217542189188279
root_relative_squared_error
0.8208829496555119
root_relative_squared_error
0.8321596431693996
root_relative_squared_error
0.82406590056698
root_relative_squared_error
0.821849308615575
root_relative_squared_error
0.846139763699127
root_relative_squared_error
0.8398966870420698
root_relative_squared_error
0.8199966093902081
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
1814.1330079999989
usercpu_time_millis
1596.5337770000474
usercpu_time_millis
1614.9437950000447
usercpu_time_millis
1642.956378000008
usercpu_time_millis
1647.6173469999935
usercpu_time_millis
1632.8015399999458
usercpu_time_millis
1631.2339959999917
usercpu_time_millis
1620.147363000001
usercpu_time_millis
1659.5577950000688
usercpu_time_millis
1697.36873100004
usercpu_time_millis_testing
141.90508500001897
usercpu_time_millis_testing
136.37681000000157
usercpu_time_millis_testing
136.67748400001756
usercpu_time_millis_testing
141.81930099999818
usercpu_time_millis_testing
145.829305999996
usercpu_time_millis_testing
137.43556499997567
usercpu_time_millis_testing
138.09224300001688
usercpu_time_millis_testing
137.35177199998816
usercpu_time_millis_testing
136.43970700002228
usercpu_time_millis_testing
140.68998499999452
usercpu_time_millis_training
1672.22792299998
usercpu_time_millis_training
1460.1569670000458
usercpu_time_millis_training
1478.2663110000271
usercpu_time_millis_training
1501.1370770000099
usercpu_time_millis_training
1501.7880409999975
usercpu_time_millis_training
1495.3659749999701
usercpu_time_millis_training
1493.1417529999749
usercpu_time_millis_training
1482.7955910000128
usercpu_time_millis_training
1523.1180880000466
usercpu_time_millis_training
1556.6787460000455