5845518
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)
3880268
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
19.11102575126262
6953
cache_size
200
6953
class_weight
null
6953
coef0
-0.6862110190625523
6953
decision_function_shape
null
6953
degree
3
6953
gamma
2.532290648685127
6953
kernel
"sigmoid"
6953
max_iter
-1
6953
probability
true
6953
random_state
60297
6953
shrinking
true
6953
tol
0.007919585831678582
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
12615017
description
https://api.openml.org/data/download/12615017/description.xml
-1
12615019
predictions
https://api.openml.org/data/download/12615019/predictions.arff
area_under_roc_curve
0.9951207386363636 [0.993963,0.999882,1,1,0.999605,0.994949,0.99925,1,0.993805,0.999842,0.998264,0.999645,0.999724,0.976523,0.999763,1,1,0.981771,0.97676,0.97242,0.993253,0.999882,0.994634,1,0.99562,0.973761,0.975063,0.998856,0.998619,1,0.999803,1,0.999369,0.989465,0.999329,0.998856,0.986269,0.975892,0.999842,1,0.997711,0.999369,1,0.998856,0.999171,0.999211,0.999566,0.997277,0.99858,0.995975,0.996488,0.996804,0.998422,0.985874,0.997751,0.972617,1,0.999684,0.993884,0.988439,0.999092,0.998935,0.994831,0.998303,0.999171,0.992898,0.986072,0.999921,0.99345,0.9942,0.991596,0.997751,0.991438,1,0.983467,0.999605,0.999842,0.99128,1,0.999487,0.994634,0.997514,0.999329,0.995265,0.99858,0.999763,1,0.998856,0.999408,0.999803,0.993095,0.999448,0.998619,0.997159,0.999408,0.997711,0.996725,0.99779,0.961608,0.99566]
average_cost
0
f_measure
0.7809779092958028 [0.705882,0.857143,1,1,0.9375,0.416667,0.9375,0.967742,0.631579,0.896552,0.8,0.969697,0.967742,0.625,1,0.969697,1,0.222222,0.37037,0.121212,0.533333,0.9375,0.684211,1,0.8125,0.322581,0.341463,0.8125,0.903226,0.896552,0.967742,0.967742,0.866667,0.470588,0.896552,0.666667,0.4,0.342857,0.875,0.967742,0.647059,0.9375,0.933333,0.8125,0.9375,0.848485,0.866667,0.8125,0.756757,0.83871,0.777778,0.758621,0.866667,0.8,0.787879,0.294118,1,0.903226,0.722222,0.742857,0.733333,0.875,0.705882,0.848485,0.903226,0.625,0.3125,1,0.75,0.75,0.642857,0.8,0.611111,1,0.8125,1,0.903226,0.444444,1,0.9375,0.774194,0.933333,0.909091,0.666667,0.875,0.875,0.967742,0.83871,0.909091,1,0.785714,0.785714,0.848485,0.875,0.9375,0.823529,0.857143,0.705882,0.235294,0.733333]
kappa
0.7765151515151516
kb_relative_information_score
968.8695821507412
mean_absolute_error
0.016294466058817653
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
0.7903656339274757 [0.666667,0.789474,1,1,0.9375,0.625,0.9375,1,0.545455,1,0.857143,0.941176,1,0.625,1,0.941176,1,0.272727,0.454545,0.117647,0.571429,0.9375,0.590909,1,0.8125,0.333333,0.28,0.8125,0.933333,1,1,1,0.928571,0.444444,1,0.714286,0.368421,0.315789,0.875,1,0.611111,0.9375,1,0.8125,0.9375,0.823529,0.928571,0.8125,0.666667,0.866667,0.7,0.846154,0.928571,0.857143,0.764706,0.277778,1,0.933333,0.65,0.684211,0.785714,0.875,0.666667,0.823529,0.933333,0.625,0.3125,1,0.75,0.75,0.75,0.857143,0.55,1,0.8125,1,0.933333,0.4,1,0.9375,0.8,1,0.882353,0.714286,0.875,0.875,1,0.866667,0.882353,1,0.916667,0.916667,0.823529,0.875,0.9375,0.777778,0.789474,0.666667,0.222222,0.785714]
predictive_accuracy
0.77875
prior_entropy
6.6438561897747395
recall
0.77875 [0.75,0.9375,1,1,0.9375,0.3125,0.9375,0.9375,0.75,0.8125,0.75,1,0.9375,0.625,1,1,1,0.1875,0.3125,0.125,0.5,0.9375,0.8125,1,0.8125,0.3125,0.4375,0.8125,0.875,0.8125,0.9375,0.9375,0.8125,0.5,0.8125,0.625,0.4375,0.375,0.875,0.9375,0.6875,0.9375,0.875,0.8125,0.9375,0.875,0.8125,0.8125,0.875,0.8125,0.875,0.6875,0.8125,0.75,0.8125,0.3125,1,0.875,0.8125,0.8125,0.6875,0.875,0.75,0.875,0.875,0.625,0.3125,1,0.75,0.75,0.5625,0.75,0.6875,1,0.8125,1,0.875,0.5,1,0.9375,0.75,0.875,0.9375,0.625,0.875,0.875,0.9375,0.8125,0.9375,1,0.6875,0.6875,0.875,0.875,0.9375,0.875,0.9375,0.75,0.25,0.6875]
relative_absolute_error
0.8229528312534155
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08372503767012587
root_relative_squared_error
0.8414682894247847
total_cost
0
area_under_roc_curve
0.9939554971737922 [0.981132,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,0.952532,1,1,1,0.996835,0.968354,0.981132,0.981132,1,1,1,0.993711,0.990506,0.984177,1,1,1,1,1,0.996835,0.977848,0.996835,0.987421,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.96519,1,1,1,1,1,0.981132,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.918239,0.984177,1,1,1,1,1,1,0.974684,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,0.987342,0.993711,0.990506,0.879747,1]
area_under_roc_curve
0.9949434260807257 [1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.920886,1,1,1,0.974684,0.993671,0.981132,1,1,1,1,1,0.977848,0.977848,1,1,1,1,1,0.996835,1,1,0.993711,0.993711,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.968553,1,1,1,0.977848,1,1,0.996835,1,1,0.993671,0.993671,1,0.937107,1,0.984177,1,1,1,1,1,1,0.990506,1,0.993671,0.977848,1,1,1,0.993671,1,1,1,1,1,0.977848,1,1,0.990506,1,1,1,0.993671,0.984177,0.987421]
area_under_roc_curve
0.9944346588647399 [1,1,1,1,1,0.993671,0.993711,1,0.990506,1,1,1,1,0.996835,1,1,1,0.955696,0.987421,0.949367,0.974843,1,0.974684,1,1,0.974684,0.977848,0.993711,1,1,1,1,1,0.987342,1,1,0.987421,0.899371,1,1,0.993671,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.974843,0.971519,1,1,1,0.968553,0.996835,0.996835,0.996835,1,0.993711,0.996835,0.987342,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.924528,1]
area_under_roc_curve
0.9943169831223628 [0.996835,1,1,1,1,0.990506,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.91195,0.971519,1,1,0.996835,1,0.974843,0.977848,0.971519,1,1,1,1,1,1,0.993671,1,1,0.993711,0.930818,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,0.962025,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.968354,1,1,0.993671,0.981013,0.996835,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,0.987342,0.993671,0.893082,0.977848]
area_under_roc_curve
0.995457169015206 [1,1,1,1,1,1,1,1,0.987421,1,0.993671,1,1,0.943396,1,1,1,0.993671,0.987421,0.987342,1,1,1,1,0.990506,0.993671,0.981013,1,1,1,1,1,1,0.981013,1,1,0.993671,0.968354,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.974843,1,0.993711,0.987342,0.987342,1,0.996835,1,1,1,1,1,1,1,1,0.987421,1,0.993671,0.990506,1,1,1,1,0.914557,1,0.993671,0.987342,1,1,1,1,1,0.990506,0.987421,1,1,1,1,1,1,0.996835,1,1,0.993671,0.993711,1,1,0.993711,1]
area_under_roc_curve
0.995494984475758 [0.96519,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.981132,0.946203,1,1,0.990506,1,0.993671,0.981013,0.981013,1,1,1,1,1,1,0.993671,1,0.996835,0.987342,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.974684,1,1,0.996835,0.993711,0.993671,1,0.987421,1,1,0.990506,1,1,1,1,0.996835,0.990506,1,1,0.990506,1,1,0.990506,1,1,0.987421,0.993671,1,0.987342,1,1,1,1,1,1,0.974843,1,1,1,1,1,1,1,0.962264,0.987342]
area_under_roc_curve
0.9953457129209455 [1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,0.993711,0.974684,0.96519,0.993671,1,1,1,1,0.861635,0.893082,0.996835,1,1,0.996835,1,1,0.993711,1,1,0.96519,0.984177,1,1,1,1,1,1,0.987421,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,0.984177,1,1,0.996835,1,1,0.996835,1,0.996835,1,0.968553,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.981132,1,1,0.981013,1,1,1,1,1,1,0.993711,1,1,1,1,0.990506,1,1,1,1,1,0.993671,1]
area_under_roc_curve
0.9965670030252369 [1,1,1,1,1,1,1,1,0.987421,1,0.987421,1,1,1,1,1,1,0.949686,0.971519,0.993671,0.993671,1,0.993671,1,0.993671,0.981132,0.987421,1,0.993711,1,1,1,1,1,1,1,0.981013,0.993671,1,1,0.996835,1,1,1,1,1,1,0.990506,1,0.993711,0.968553,0.996835,1,1,1,0.962025,1,1,0.990506,1,1,1,1,1,1,0.974843,0.962264,1,0.996835,1,1,1,0.996835,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1]
area_under_roc_curve
0.9958534849932328 [1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993711,0.996835,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.984177,1,1,0.993711,1,1,0.990506,1,0.993671,1,0.993671,0.993711,0.993711,0.993671,0.996835,1,0.936709,0.996835,0.918239,1,1,0.993671,1,0.996835,1,1,1,1,0.993711,0.981013,1,0.993671,1,1,0.987421,0.955696,1,1,1,1,0.993711,1,0.996835,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,0.993711,1,0.984177,1]
area_under_roc_curve
0.9967244845155637 [1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,1,0.990506,1,1,1,0.943396,0.977848,0.974843,0.981013,1,1,1,1,0.981132,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.987421,1,1,0.987342,0.990506,1,1,0.984177,1,0.993711,0.993711,0.977848,1,1,0.993711,1,1,0.984177,1,0.993671,0.996835,1,1,1,1,1,0.974843,1,0.987421,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,0.987421,1,1,1]
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
0.772592680326898
kappa
0.7914938988271532
kappa
0.8041538340045803
kappa
0.7662850375049349
kappa
0.7598925835242082
kappa
0.7283745903904615
kappa
0.7852094602598018
kappa
0.7599210266535044
kappa
0.7978521794061908
kappa
0.797876120168963
kb_relative_information_score
97.00995450111265
kb_relative_information_score
95.95643910005656
kb_relative_information_score
96.55196942745981
kb_relative_information_score
96.24450918683971
kb_relative_information_score
96.41800065766522
kb_relative_information_score
95.00941681627236
kb_relative_information_score
99.17628659926217
kb_relative_information_score
97.47743887847894
kb_relative_information_score
97.45186595804782
kb_relative_information_score
97.57370102554525
mean_absolute_error
0.01622953481837832
mean_absolute_error
0.016383614862576147
mean_absolute_error
0.016330818020234575
mean_absolute_error
0.016331575389392287
mean_absolute_error
0.016394130127676942
mean_absolute_error
0.016521320287470893
mean_absolute_error
0.016004703914971784
mean_absolute_error
0.016264450269625787
mean_absolute_error
0.016244283232621472
mean_absolute_error
0.01624022966522825
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
0.775
predictive_accuracy
0.79375
predictive_accuracy
0.80625
predictive_accuracy
0.76875
predictive_accuracy
0.7625
predictive_accuracy
0.73125
predictive_accuracy
0.7875
predictive_accuracy
0.7625
predictive_accuracy
0.8
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
0.775 [1,0.5,1,1,1,0,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,0,0,1,1,1,0,0.5,0.5,1,1,0,1,1,0.5,0,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0,1]
recall
0.79375 [1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,0,0.5,1,0.5,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,0]
recall
0.80625 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,1]
recall
0.76875 [1,1,1,1,0.5,0,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,0.5,0,0,0,1,1,1,0,0.5,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,0.5,0,0,1,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,0]
recall
0.7625 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0,0,0.5,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,0,0,0.5,1,0.5,0,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1]
recall
0.73125 [0.5,1,1,1,1,0,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0,0,0.5,0.5,0.5,1,1,1,0,1,0,1,0.5,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0,1,0,1,1,0.5,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0]
recall
0.7875 [1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1]
recall
0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,0.5,1,0.5,0,1,1,0,1,0.5,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,0,0.5,1,1,1,1,1,0,0.5,1]
recall
0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,0,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,0,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1]
recall
0.8 [1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,0.5,1,1,1,1,0,0,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,0,1,0.5,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
relative_absolute_error
0.8196734756756713
relative_absolute_error
0.827455296089703
relative_absolute_error
0.8247887889007347
relative_absolute_error
0.8248270398682961
relative_absolute_error
0.8279863700846927
relative_absolute_error
0.8344101155288315
relative_absolute_error
0.8083183795440282
relative_absolute_error
0.8214368823043313
relative_absolute_error
0.8204183450818913
relative_absolute_error
0.8202136194559708
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
0.08352163699474857
root_mean_squared_error
0.08397869074649027
root_mean_squared_error
0.08389582354608668
root_mean_squared_error
0.08390108252781628
root_mean_squared_error
0.0841408701356158
root_mean_squared_error
0.08495543740280602
root_mean_squared_error
0.08239437227687238
root_mean_squared_error
0.08349946909053187
root_mean_squared_error
0.08345844768795199
root_mean_squared_error
0.08348182641619283
root_relative_squared_error
0.8394240357207504
root_relative_squared_error
0.8440175987618153
root_relative_squared_error
0.8431847520613127
root_relative_squared_error
0.8432376068163927
root_relative_squared_error
0.8456475629510833
root_relative_squared_error
0.8538342720170686
root_relative_squared_error
0.8280945990280196
root_relative_squared_error
0.8392012399005221
root_relative_squared_error
0.8387889592922445
root_relative_squared_error
0.8390239243517915
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
1626.7307749999995
usercpu_time_millis
1639.908868
usercpu_time_millis
1611.3324339999995
usercpu_time_millis
1614.3888859999995
usercpu_time_millis
1612.8514119999995
usercpu_time_millis
1627.3316020000016
usercpu_time_millis
1619.9294959999993
usercpu_time_millis
1637.5569799999994
usercpu_time_millis
1642.6952340000032
usercpu_time_millis
1647.1016330000018
usercpu_time_millis_testing
166.4726489999997
usercpu_time_millis_testing
164.8102109999998
usercpu_time_millis_testing
163.83091299999995
usercpu_time_millis_testing
161.9228029999995
usercpu_time_millis_testing
163.33141500000005
usercpu_time_millis_testing
167.65259100000128
usercpu_time_millis_testing
160.76099899999895
usercpu_time_millis_testing
170.82822399999918
usercpu_time_millis_testing
163.33295300000117
usercpu_time_millis_testing
167.85321300000078
usercpu_time_millis_training
1460.258126
usercpu_time_millis_training
1475.0986570000002
usercpu_time_millis_training
1447.5015209999995
usercpu_time_millis_training
1452.466083
usercpu_time_millis_training
1449.5199969999994
usercpu_time_millis_training
1459.6790110000004
usercpu_time_millis_training
1459.1684970000003
usercpu_time_millis_training
1466.7287560000002
usercpu_time_millis_training
1479.362281000002
usercpu_time_millis_training
1479.248420000001