5959249
1
Jan van Rijn
9954
Supervised Classification
6952
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1)
3993990
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"most_frequent"
6947
strategy_nominal
"most_frequent"
6947
verbose
0
6947
categorical_features
[]
6948
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
6948
handle_unknown
"ignore"
6948
n_values
"auto"
6948
sparse
false
6948
threshold
0.0
6949
C
26469.36735465033
6953
cache_size
200
6953
class_weight
null
6953
coef0
-0.7039307886873287
6953
decision_function_shape
null
6953
degree
1
6953
gamma
0.30673401077557416
6953
kernel
"poly"
6953
max_iter
-1
6953
probability
true
6953
random_state
57301
6953
shrinking
true
6953
tol
0.00261821528812489
6953
verbose
false
6953
openml-pimp
openml-python
Sklearn_0.18.1.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12841921
description
https://api.openml.org/data/download/12841921/description.xml
-1
12841922
predictions
https://api.openml.org/data/download/12841922/predictions.arff
area_under_roc_curve
0.9964701704545453 [0.994318,1,1,1,0.999645,0.996646,0.999605,0.999961,0.997514,0.999921,0.998935,1,0.999803,0.985006,0.999724,1,1,0.993411,0.979798,0.978417,0.997948,0.999882,0.995975,1,0.995265,0.97309,0.984612,0.998619,0.998974,1,0.999842,1,0.999882,0.9884,0.999803,0.999132,0.993174,0.983704,0.999961,1,0.999211,0.999921,1,0.999014,0.999448,0.999605,0.999803,0.99708,0.998224,0.997909,0.996686,0.998935,0.999132,0.98836,0.997514,0.990609,1,0.999763,0.993213,0.99416,0.998974,0.999487,0.995818,0.99858,0.999408,0.992858,0.988676,0.999921,0.994239,0.994871,0.995462,0.998185,0.993529,0.999961,0.988597,0.999724,0.999882,0.991951,1,0.999684,0.992779,0.997514,0.999132,0.994476,0.998619,1,1,0.999527,0.999448,1,0.997041,0.999448,0.999014,0.998422,0.999487,0.997514,0.999171,0.998777,0.970249,0.997041]
average_cost
0
f_measure
0.826489669451056 [0.722222,0.967742,1,1,0.9375,0.827586,0.969697,0.967742,0.875,0.857143,0.9375,0.941176,0.967742,0.758621,1,0.9375,1,0.533333,0.333333,0.324324,0.8125,0.9375,0.6875,1,0.8,0.413793,0.5,0.8125,0.866667,0.896552,0.969697,1,0.875,0.5,0.941176,0.740741,0.580645,0.545455,0.903226,0.967742,0.882353,0.941176,0.967742,0.848485,0.967742,0.933333,0.9375,0.83871,0.722222,0.903226,0.823529,0.814815,0.909091,0.758621,0.8125,0.514286,1,0.9375,0.666667,0.722222,0.8,0.9375,0.722222,0.909091,0.903226,0.571429,0.533333,1,0.787879,0.764706,0.709677,0.827586,0.5,0.967742,0.764706,0.969697,0.933333,0.516129,1,0.9375,0.8125,0.896552,0.903226,0.592593,0.83871,0.866667,0.933333,0.848485,0.9375,1,0.866667,0.866667,0.875,0.967742,1,0.833333,0.882353,0.857143,0.4375,0.866667]
kappa
0.82260101010101
kb_relative_information_score
983.1681846708555
mean_absolute_error
0.016163580486212463
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
0.8358607842311738 [0.65,1,1,1,0.9375,0.923077,0.941176,1,0.875,1,0.9375,0.888889,1,0.846154,1,0.9375,1,0.571429,0.357143,0.285714,0.8125,0.9375,0.6875,1,0.736842,0.461538,0.416667,0.8125,0.928571,1,0.941176,1,0.875,0.45,0.888889,0.909091,0.6,0.529412,0.933333,1,0.833333,0.888889,1,0.823529,1,1,0.9375,0.866667,0.65,0.933333,0.777778,1,0.882353,0.846154,0.8125,0.473684,1,0.9375,0.565217,0.65,0.857143,0.9375,0.65,0.882353,0.933333,0.526316,0.571429,1,0.764706,0.722222,0.733333,0.923077,0.5,1,0.722222,0.941176,1,0.533333,1,0.9375,0.8125,1,0.933333,0.727273,0.866667,0.928571,1,0.823529,0.9375,1,0.928571,0.928571,0.875,1,1,0.75,0.833333,0.789474,0.4375,0.928571]
predictive_accuracy
0.824375
prior_entropy
6.6438561897747395
recall
0.824375 [0.8125,0.9375,1,1,0.9375,0.75,1,0.9375,0.875,0.75,0.9375,1,0.9375,0.6875,1,0.9375,1,0.5,0.3125,0.375,0.8125,0.9375,0.6875,1,0.875,0.375,0.625,0.8125,0.8125,0.8125,1,1,0.875,0.5625,1,0.625,0.5625,0.5625,0.875,0.9375,0.9375,1,0.9375,0.875,0.9375,0.875,0.9375,0.8125,0.8125,0.875,0.875,0.6875,0.9375,0.6875,0.8125,0.5625,1,0.9375,0.8125,0.8125,0.75,0.9375,0.8125,0.9375,0.875,0.625,0.5,1,0.8125,0.8125,0.6875,0.75,0.5,0.9375,0.8125,1,0.875,0.5,1,0.9375,0.8125,0.8125,0.875,0.5,0.8125,0.8125,0.875,0.875,0.9375,1,0.8125,0.8125,0.875,0.9375,1,0.9375,0.9375,0.9375,0.4375,0.8125]
relative_absolute_error
0.8163424487986077
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08291669221965473
root_relative_squared_error
0.8333441119696222
total_cost
0
area_under_roc_curve
0.996680698192819 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,0.96519,0.987421,0.993711,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.962264,0.996835,1,1,1,1,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.984177,1,0.990506,0.924051,1]
area_under_roc_curve
0.9968003642225934 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,1,1,0.990506,0.996835,0.993711,1,1,1,1,1,0.971519,0.993671,1,1,1,1,1,1,0.996835,1,0.993711,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,0.993671,1,0.949686,1,0.996835,1,0.996835,1,1,1,1,0.993671,1,0.993671,0.974684,0.996835,0.993711,0.987421,0.996835,1,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.993671,1,0.987421]
area_under_roc_curve
0.9968409163283181 [1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993671,0.993711,0.962025,0.993711,1,0.987342,1,1,0.952532,0.996835,0.993711,1,1,1,1,1,0.990506,1,1,0.993711,0.968553,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.987342,1,1,1,0.974843,0.996835,0.996835,1,1,0.987421,0.996835,0.993671,0.996835,1,1,0.993711,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1]
area_under_roc_curve
0.9958161671045298 [1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,1,1,1,1,1,0.996835,0.90566,0.974684,1,1,1,1,0.974843,0.990506,0.981013,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,0.993671,1,1,1,1,1,0.993671,0.974684,1,1,0.990506,0.977848,0.996835,0.987421,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.949686,0.987342]
area_under_roc_curve
0.9966030769843165 [1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.949686,1,1,1,0.996835,1,0.984177,1,1,1,1,0.993671,1,0.977848,1,1,1,1,1,1,0.974684,1,1,0.993671,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.981132,1,1,0.990506,0.996835,1,0.996835,1,0.993711,1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.946203,1,0.996835,0.984177,1,1,1,1,1,0.996835,0.987421,1,1,1,1,1,1,0.996835,1,1,0.996835,0.993711,1,1,0.987421,1]
area_under_roc_curve
0.9968767415014728 [0.958861,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.962025,1,1,0.993671,1,0.993671,0.990506,0.993671,1,1,1,1,1,1,0.993671,1,1,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.977848,1,1,1,1,0.993671,1,0.987421,1,1,0.987342,1,1,1,1,1,0.987342,1,1,0.987342,1,1,0.996835,1,1,0.987421,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671]
area_under_roc_curve
0.9956586856142025 [1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.968354,0.996835,1,1,1,1,0.886792,0.930818,0.993671,1,1,0.996835,1,1,0.987421,1,1,0.971519,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,1,0.993671,1,1,1,1,0.996835,1,0.981132,0.987421,1,1,1,1,1,0.993671,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.933544,1]
area_under_roc_curve
0.997514131040522 [1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,0.974684,1,1,1,1,1,0.993671,0.974843,0.987421,1,0.993711,1,1,1,1,0.993711,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.984177,1,1,0.974843,1,1,1,1,0.981013,1,1,0.993671,1,1,1,1,1,1,0.981132,0.949686,1,0.993671,1,1,1,0.996835,1,1,1,1,1,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1]
area_under_roc_curve
0.9971173174906456 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.993711,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,1,1,0.996835,0.96519,1,1,1,1,1,0.993671,1,0.990506,1,1,0.993711,1,0.993671,1,1,0.936709,0.996835,0.955975,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,0.987421,0.974684,1,1,1,1,0.987421,1,0.996835,1,0.993711,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9978691485550513 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.981132,0.974684,0.968553,0.996835,1,1,1,1,0.993711,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.990506,0.990506,1,1,0.987342,1,0.993711,0.993711,0.993671,1,1,0.993711,1,1,0.993671,1,0.996835,1,1,1,1,1,1,0.981132,1,0.993711,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.993711,1,0.996835,1]
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
0.8357937949001343
kappa
0.8357354392892399
kappa
0.86101788605046
kappa
0.7978840991631139
kappa
0.7851755321249456
kappa
0.8230787457546797
kappa
0.8167963043392427
kappa
0.8230927183699257
kappa
0.8420533070088845
kappa
0.8042156785347754
kb_relative_information_score
99.15708330851318
kb_relative_information_score
97.29137839318032
kb_relative_information_score
98.7267392933553
kb_relative_information_score
97.13732475656258
kb_relative_information_score
97.26968911875296
kb_relative_information_score
96.20189757252749
kb_relative_information_score
100.71003756378002
kb_relative_information_score
98.71896286066944
kb_relative_information_score
98.9786670409667
kb_relative_information_score
98.9764047625472
mean_absolute_error
0.016051443487241353
mean_absolute_error
0.016280875272180587
mean_absolute_error
0.01614327086713402
mean_absolute_error
0.01627590884455137
mean_absolute_error
0.01629974090364054
mean_absolute_error
0.01640507375910049
mean_absolute_error
0.01586979677442186
mean_absolute_error
0.01612994338779992
mean_absolute_error
0.0160795417185101
mean_absolute_error
0.016100209847544372
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
0.8375
predictive_accuracy
0.8375
predictive_accuracy
0.8625
predictive_accuracy
0.8
predictive_accuracy
0.7875
predictive_accuracy
0.825
predictive_accuracy
0.81875
predictive_accuracy
0.825
predictive_accuracy
0.84375
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
0.8375 [1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1]
recall
0.8375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,1,1,1,0.5,1,1,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0]
recall
0.8625 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.8 [1,1,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,0.5,0,0.5,0,0.5,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,0.5]
recall
0.7875 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0.5,1,0,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1,1,0,1,0,1,0.5,1,1,0,0,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,0,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1]
recall
0.825 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,0,1,0.5,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5]
recall
0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,0.5,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1]
recall
0.825 [0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1]
recall
0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1]
recall
0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
relative_absolute_error
0.810678964002087
relative_absolute_error
0.8222664278879072
relative_absolute_error
0.8153167104613126
relative_absolute_error
0.8220155982096636
relative_absolute_error
0.8232192375576016
relative_absolute_error
0.8285390787424477
relative_absolute_error
0.8015048875970622
relative_absolute_error
0.814643605444439
relative_absolute_error
0.8120980665914179
relative_absolute_error
0.8131419114921385
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
0.08239874529757661
root_mean_squared_error
0.08333916119401606
root_mean_squared_error
0.08276295974037585
root_mean_squared_error
0.0835088689401401
root_mean_squared_error
0.08355061026197852
root_mean_squared_error
0.08414381885221021
root_mean_squared_error
0.08157396230844523
root_mean_squared_error
0.08269252100095476
root_mean_squared_error
0.08250914802499118
root_mean_squared_error
0.08265809206918787
root_relative_squared_error
0.8281385495397662
root_relative_squared_error
0.8375900849196916
root_relative_squared_error
0.8317990424185342
root_relative_squared_error
0.8392957119436624
root_relative_squared_error
0.8397152280127274
root_relative_squared_error
0.8456771986679219
root_relative_squared_error
0.8198491686051704
root_relative_squared_error
0.8310911064507601
root_relative_squared_error
0.8292481386993602
root_relative_squared_error
0.8307450826671133
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
1043.4135059999994
usercpu_time_millis
1047.938919
usercpu_time_millis
1047.5915630000006
usercpu_time_millis
1039.3956710000002
usercpu_time_millis
1048.7919440000005
usercpu_time_millis
1042.2184030000024
usercpu_time_millis
1049.4749669999983
usercpu_time_millis
1051.3488669999997
usercpu_time_millis
1059.063929999999
usercpu_time_millis
1052.784172999999
usercpu_time_millis_testing
136.84193399999955
usercpu_time_millis_testing
132.06702000000004
usercpu_time_millis_testing
132.53725100000048
usercpu_time_millis_testing
132.33068999999986
usercpu_time_millis_testing
130.04074000000008
usercpu_time_millis_testing
130.0757500000014
usercpu_time_millis_testing
127.00535499999965
usercpu_time_millis_testing
131.11116199999984
usercpu_time_millis_testing
133.64231499999946
usercpu_time_millis_testing
130.88650499999943
usercpu_time_millis_training
906.5715719999998
usercpu_time_millis_training
915.8718989999999
usercpu_time_millis_training
915.0543120000001
usercpu_time_millis_training
907.0649810000004
usercpu_time_millis_training
918.7512040000004
usercpu_time_millis_training
912.142653000001
usercpu_time_millis_training
922.4696119999986
usercpu_time_millis_training
920.2377049999999
usercpu_time_millis_training
925.4216149999994
usercpu_time_millis_training
921.8976679999997