8844411
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)
6819461
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"
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
0.11411667275718168
7708
cache_size
200
7708
class_weight
null
7708
coef0
-0.16358281402067099
7708
decision_function_shape
null
7708
degree
3
7708
gamma
0.7151874086591404
7708
kernel
"poly"
7708
max_iter
-1
7708
probability
true
7708
random_state
50133
7708
shrinking
true
7708
tol
0.0012164592182003363
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
18604462
description
https://api.openml.org/data/download/18604462/description.xml
-1
18604463
predictions
https://api.openml.org/data/download/18604463/predictions.arff
area_under_roc_curve
0.996266966540404 [0.98694,0.999803,1,0.999763,0.99854,0.993647,0.999763,0.999132,0.997396,0.999921,0.998777,0.999645,0.999329,0.967685,0.999053,0.999369,1,0.991319,0.984691,0.974313,0.997988,0.999921,0.995857,0.999171,0.998698,0.977233,0.991359,0.999132,0.998658,1,0.999605,0.999961,1,0.990846,0.998737,0.998737,0.993726,0.983152,0.999408,0.999763,0.998816,0.997909,1,0.998895,0.99633,1,0.999171,0.999211,0.997593,0.996962,0.994673,0.998895,0.998106,0.992582,0.996488,0.992779,1,0.999448,0.99349,0.996528,0.997988,0.999408,0.997238,0.99929,0.999171,0.993647,0.985598,0.999882,0.997988,0.993963,0.996646,0.999132,0.995541,0.998264,0.989662,0.999408,0.999369,0.99128,0.999684,1,0.996449,0.997199,0.997948,0.995581,0.999369,0.999961,0.999961,0.999132,0.999171,0.999882,0.999408,0.999605,0.998382,0.997356,0.995896,0.997435,0.99783,0.998264,0.969026,0.996765]
average_cost
0
f_measure
0.8200026710717434 [0.648649,0.9375,0.967742,0.933333,0.933333,0.774194,0.969697,0.823529,0.8,0.896552,0.848485,0.903226,0.909091,0.714286,0.933333,0.903226,0.967742,0.5625,0.424242,0.2,0.774194,0.9375,0.764706,0.9375,0.882353,0.411765,0.529412,0.909091,0.903226,0.967742,0.9375,0.967742,0.9375,0.588235,0.875,0.875,0.5625,0.533333,0.909091,0.896552,0.8,0.875,0.933333,0.848485,0.903226,0.941176,0.857143,0.875,0.727273,0.8125,0.848485,0.774194,0.8125,0.666667,0.733333,0.580645,0.967742,0.9375,0.666667,0.742857,0.903226,0.833333,0.709677,0.9375,0.909091,0.722222,0.484848,0.9375,0.866667,0.75,0.787879,0.896552,0.6875,0.967742,0.789474,0.9375,0.903226,0.685714,1,1,0.8125,0.8125,0.83871,0.533333,0.875,0.933333,0.967742,0.866667,0.903226,0.967742,0.8125,0.903226,0.866667,0.83871,0.9375,0.8,0.8,0.83871,0.625,0.903226]
kappa
0.8162878787878788
kb_relative_information_score
983.4862125512041
mean_absolute_error
0.01589277916949994
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
0.8270500402366062 [0.571429,0.9375,1,1,1,0.8,0.941176,0.777778,0.857143,1,0.823529,0.933333,0.882353,0.833333,1,0.933333,1,0.5625,0.411765,0.214286,0.8,0.9375,0.722222,0.9375,0.833333,0.388889,0.5,0.882353,0.933333,1,0.9375,1,0.9375,0.555556,0.875,0.875,0.5625,0.571429,0.882353,1,0.736842,0.875,1,0.823529,0.933333,0.888889,1,0.875,0.705882,0.8125,0.823529,0.8,0.8125,0.647059,0.785714,0.6,1,0.9375,0.565217,0.684211,0.933333,0.75,0.733333,0.9375,0.882353,0.65,0.470588,0.9375,0.928571,0.75,0.764706,1,0.6875,1,0.681818,0.9375,0.933333,0.631579,1,1,0.8125,0.8125,0.866667,0.571429,0.875,1,1,0.928571,0.933333,1,0.8125,0.933333,0.928571,0.866667,0.9375,0.736842,0.736842,0.866667,0.625,0.933333]
predictive_accuracy
0.818125
prior_entropy
6.6438561897747395
recall
0.818125 [0.75,0.9375,0.9375,0.875,0.875,0.75,1,0.875,0.75,0.8125,0.875,0.875,0.9375,0.625,0.875,0.875,0.9375,0.5625,0.4375,0.1875,0.75,0.9375,0.8125,0.9375,0.9375,0.4375,0.5625,0.9375,0.875,0.9375,0.9375,0.9375,0.9375,0.625,0.875,0.875,0.5625,0.5,0.9375,0.8125,0.875,0.875,0.875,0.875,0.875,1,0.75,0.875,0.75,0.8125,0.875,0.75,0.8125,0.6875,0.6875,0.5625,0.9375,0.9375,0.8125,0.8125,0.875,0.9375,0.6875,0.9375,0.9375,0.8125,0.5,0.9375,0.8125,0.75,0.8125,0.8125,0.6875,0.9375,0.9375,0.9375,0.875,0.75,1,1,0.8125,0.8125,0.8125,0.5,0.875,0.875,0.9375,0.8125,0.875,0.9375,0.8125,0.875,0.8125,0.8125,0.9375,0.875,0.875,0.8125,0.625,0.875]
relative_absolute_error
0.8026656146212078
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08206766451239048
root_relative_squared_error
0.8248110624495882
total_cost
0
area_under_roc_curve
0.9965630224504419 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,0.981132,1,0.996835,0.977848,0.987421,0.993711,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,0.962264,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.946203,1]
area_under_roc_curve
0.9962458203964653 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.889241,1,1,1,0.981013,0.993671,0.987421,1,0.993711,0.993711,1,1,0.984177,0.971519,1,1,1,1,1,1,0.996835,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981013,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.974843]
area_under_roc_curve
0.9967195087970703 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,1,1,1,0.996835,1,0.939873,1,1,0.993671,1,1,0.936709,0.993671,1,1,1,1,1,1,0.984177,1,1,1,1,0.993711,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,1,1,0.987421,1,0.990506,1,1,1,0.987342,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1]
area_under_roc_curve
0.9971178150624948 [1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.984177,0.943396,0.981013,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.993671,1,1,0.977848,0.984177,1,0.987421,1,0.993711,1,1,0.996835,1,1,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,1]
area_under_roc_curve
0.9961308812992595 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.918239,1,1,1,0.993671,0.987421,0.987342,1,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.981013,1,1,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.987342,1,0.996835,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.949367,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.937107,1]
area_under_roc_curve
0.9962032780033436 [0.917722,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,1,1,1,0.993671,0.990506,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,1,0.987342,0.996835,1,0.993671,1,1,0.993711,1,1,1,1,0.990506,0.993671,1,1,0.993671,1,1,0.987342,1,1,0.996835,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835]
area_under_roc_curve
0.9955773326168298 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.971519,0.996835,1,0.990506,1,1,0.893082,0.968553,0.993671,1,1,0.996835,1,1,1,1,1,0.971519,0.993671,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.867089,1]
area_under_roc_curve
0.9972797746994664 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.993711,0.993671,0.990506,0.996835,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.993671,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,0.955975,1,1,1,1,0.990506,1,1,0.993671,1,1,1,1,1,1,0.974843,0.930818,1,1,0.996835,1,1,1,1,1,1,1,0.968553,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671]
area_under_roc_curve
0.996998397818645 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.990506,0.974843,0.996835,1,0.974843,0.993671,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.952532,1,1,1,1,1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.974684,0.984177,0.993711,1,1,1,1,0.993671,1,1,1,1,1,0.987342,1,1,1,1,0.993711,0.981013,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,0.996835,1]
area_under_roc_curve
0.9976300652814271 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.968354,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,1,0.993711,0.993711,0.984177,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.996835,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1]
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
0.785234899328859
kappa
0.7914938988271532
kappa
0.87366260018161
kappa
0.7915268290756899
kappa
0.8230787457546797
kappa
0.8357419252941641
kappa
0.7978282329713722
kappa
0.8420221169036335
kappa
0.8231136731551308
kappa
0.7978920775273359
kb_relative_information_score
99.22522390561764
kb_relative_information_score
96.77585619455358
kb_relative_information_score
97.84703453211456
kb_relative_information_score
96.64707210846791
kb_relative_information_score
97.83098600226293
kb_relative_information_score
97.8635239715927
kb_relative_information_score
101.79745061029418
kb_relative_information_score
98.32031357800732
kb_relative_information_score
98.62748957176622
kb_relative_information_score
98.55126207652745
mean_absolute_error
0.01571886161801741
mean_absolute_error
0.01612989202212981
mean_absolute_error
0.016037369151090605
mean_absolute_error
0.016171902062688117
mean_absolute_error
0.0160743928173143
mean_absolute_error
0.015901812934463255
mean_absolute_error
0.015374871149944017
mean_absolute_error
0.015951834937929073
mean_absolute_error
0.015804283444605487
mean_absolute_error
0.01576257155681733
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
0.7875
predictive_accuracy
0.79375
predictive_accuracy
0.875
predictive_accuracy
0.79375
predictive_accuracy
0.825
predictive_accuracy
0.8375
predictive_accuracy
0.8
predictive_accuracy
0.84375
predictive_accuracy
0.825
predictive_accuracy
0.8
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
0.7875 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,1,0,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1]
recall
0.79375 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0]
recall
0.875 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1]
recall
0.79375 [0,1,1,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,0,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1]
recall
0.825 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1]
recall
0.8375 [0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1]
recall
0.8 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,1,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5]
recall
0.825 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0.5,1,0.5,1,1,1,0,1,0.5,1,1,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1]
recall
0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1]
relative_absolute_error
0.7938818998998677
relative_absolute_error
0.814641011218676
relative_absolute_error
0.8099681389439685
relative_absolute_error
0.8167627304387924
relative_absolute_error
0.8118380210764785
relative_absolute_error
0.8031218653769308
relative_absolute_error
0.7765086439365654
relative_absolute_error
0.8056482291883357
relative_absolute_error
0.7981961335659323
relative_absolute_error
0.7960894725665305
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
0.08138729697904723
root_mean_squared_error
0.08293672093590165
root_mean_squared_error
0.08243362984751246
root_mean_squared_error
0.08324186746065317
root_mean_squared_error
0.08262085597938496
root_mean_squared_error
0.0822434431207363
root_mean_squared_error
0.07993641640147657
root_mean_squared_error
0.08204875880159473
root_mean_squared_error
0.08184675707628651
root_mean_squared_error
0.08193412632619324
root_relative_squared_error
0.8179731114567425
root_relative_squared_error
0.833545408141815
root_relative_squared_error
0.8284891524583045
root_relative_squared_error
0.836612246107517
root_relative_squared_error
0.8303708458836703
root_relative_squared_error
0.8265777039346003
root_relative_squared_error
0.8033912129979061
root_relative_squared_error
0.8246210529068477
root_relative_squared_error
0.822590859180024
root_relative_squared_error
0.8234689531804951
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
1628.497210003843
usercpu_time_millis
1435.5021709998255
usercpu_time_millis
1419.567355995241
usercpu_time_millis
1425.5653560030623
usercpu_time_millis
1429.1938739988836
usercpu_time_millis
1433.387151992065
usercpu_time_millis
1429.0969889989356
usercpu_time_millis
1423.6436190039967
usercpu_time_millis
1430.6713640035014
usercpu_time_millis
1431.1511100022472
usercpu_time_millis_testing
134.54500799707603
usercpu_time_millis_testing
141.07720400352264
usercpu_time_millis_testing
133.50044300023
usercpu_time_millis_testing
132.8125750005711
usercpu_time_millis_testing
133.04613500076812
usercpu_time_millis_testing
131.77636199543485
usercpu_time_millis_testing
133.4091280004941
usercpu_time_millis_testing
132.60474699927727
usercpu_time_millis_testing
132.47578000300564
usercpu_time_millis_testing
132.19331899745157
usercpu_time_millis_training
1493.952202006767
usercpu_time_millis_training
1294.4249669963028
usercpu_time_millis_training
1286.066912995011
usercpu_time_millis_training
1292.7527810024912
usercpu_time_millis_training
1296.1477389981155
usercpu_time_millis_training
1301.61078999663
usercpu_time_millis_training
1295.6878609984415
usercpu_time_millis_training
1291.0388720047195
usercpu_time_millis_training
1298.1955840004957
usercpu_time_millis_training
1298.9577910047956