5948393
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)
3983138
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"mean"
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
6975.68903191981
6953
cache_size
200
6953
class_weight
null
6953
coef0
0.0
6953
decision_function_shape
null
6953
degree
3
6953
gamma
0.04861840759955716
6953
kernel
"rbf"
6953
max_iter
-1
6953
probability
true
6953
random_state
60621
6953
shrinking
true
6953
tol
0.00012781278448085123
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
12820270
description
https://api.openml.org/data/download/12820270/description.xml
-1
12820271
predictions
https://api.openml.org/data/download/12820271/predictions.arff
area_under_roc_curve
0.9963604797979795 [0.994318,1,1,1,0.999684,0.996449,0.999645,0.999961,0.997475,0.999921,0.999014,1,0.999803,0.986269,0.999724,1,1,0.99274,0.979048,0.974787,0.997869,0.999803,0.996173,1,0.995423,0.972183,0.982442,0.998935,0.998974,1,0.999882,1,0.999882,0.988873,0.999763,0.999171,0.993213,0.980982,0.999961,1,0.999211,1,1,0.998935,0.999369,0.999645,0.999724,0.997199,0.998264,0.998106,0.996488,0.999014,0.999092,0.988321,0.997672,0.987689,1,0.999763,0.99345,0.994634,0.999092,0.999527,0.995975,0.998658,0.999369,0.99274,0.988163,0.999921,0.994515,0.994397,0.995265,0.998343,0.993884,0.999961,0.988873,0.999803,0.999882,0.992188,1,0.999684,0.992424,0.998146,0.999132,0.995541,0.998619,1,1,0.999645,0.999448,1,0.996133,0.999487,0.999132,0.99854,0.999487,0.997514,0.99929,0.997869,0.969736,0.996725]
average_cost
0
f_measure
0.8244753064663338 [0.722222,0.9375,1,1,0.967742,0.758621,0.969697,0.967742,0.8125,0.896552,0.9375,0.941176,0.967742,0.758621,1,0.967742,1,0.516129,0.352941,0.216216,0.8125,0.9375,0.727273,1,0.848485,0.4,0.486486,0.8125,0.903226,0.896552,0.969697,1,0.875,0.470588,0.941176,0.714286,0.413793,0.470588,0.933333,0.967742,0.882353,0.969697,0.967742,0.848485,0.967742,0.903226,0.9375,0.83871,0.722222,0.903226,0.857143,0.769231,0.882353,0.758621,0.8125,0.5,1,0.9375,0.702703,0.727273,0.8,0.9375,0.756757,0.909091,0.903226,0.571429,0.484848,1,0.8125,0.764706,0.733333,0.857143,0.545455,0.967742,0.8,0.969697,0.933333,0.625,1,0.9375,0.787879,0.933333,0.903226,0.642857,0.83871,0.933333,0.933333,0.848485,0.9375,1,0.8,0.866667,0.909091,0.909091,0.969697,0.833333,0.875,0.857143,0.333333,0.866667]
kappa
0.8207070707070707
kb_relative_information_score
981.9033812014559
mean_absolute_error
0.01617732739893312
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
0.8328277986633246 [0.65,0.9375,1,1,1,0.846154,0.941176,1,0.8125,1,0.9375,0.888889,1,0.846154,1,1,1,0.533333,0.333333,0.190476,0.8125,0.9375,0.705882,1,0.823529,0.428571,0.428571,0.8125,0.933333,1,0.941176,1,0.875,0.444444,0.888889,0.833333,0.461538,0.444444,1,1,0.833333,0.941176,1,0.823529,1,0.933333,0.9375,0.866667,0.65,0.933333,0.789474,1,0.833333,0.846154,0.8125,0.5,1,0.9375,0.619048,0.705882,0.857143,0.9375,0.666667,0.882353,0.933333,0.526316,0.470588,1,0.8125,0.722222,0.785714,1,0.529412,1,0.736842,0.941176,1,0.625,1,0.9375,0.764706,1,0.933333,0.75,0.866667,1,1,0.823529,0.9375,1,0.857143,0.928571,0.882353,0.882353,0.941176,0.75,0.875,0.789474,0.357143,0.928571]
predictive_accuracy
0.8225
prior_entropy
6.6438561897747395
recall
0.8225 [0.8125,0.9375,1,1,0.9375,0.6875,1,0.9375,0.8125,0.8125,0.9375,1,0.9375,0.6875,1,0.9375,1,0.5,0.375,0.25,0.8125,0.9375,0.75,1,0.875,0.375,0.5625,0.8125,0.875,0.8125,1,1,0.875,0.5,1,0.625,0.375,0.5,0.875,0.9375,0.9375,1,0.9375,0.875,0.9375,0.875,0.9375,0.8125,0.8125,0.875,0.9375,0.625,0.9375,0.6875,0.8125,0.5,1,0.9375,0.8125,0.75,0.75,0.9375,0.875,0.9375,0.875,0.625,0.5,1,0.8125,0.8125,0.6875,0.75,0.5625,0.9375,0.875,1,0.875,0.625,1,0.9375,0.8125,0.875,0.875,0.5625,0.8125,0.875,0.875,0.875,0.9375,1,0.75,0.8125,0.9375,0.9375,1,0.9375,0.875,0.9375,0.3125,0.8125]
relative_absolute_error
0.817036737319853
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08298186682548471
root_relative_squared_error
0.8339991414041595
total_cost
0
area_under_roc_curve
0.9962463179683146 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.96519,0.987421,0.993711,1,1,1,0.993711,0.990506,1,1,1,1,1,1,1,0.981013,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.943396,0.993671,1,1,1,1,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.984177,1,0.987342,0.914557,1]
area_under_roc_curve
0.9966819421224423 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,1,1,0.987342,1,0.987421,1,1,1,1,1,0.977848,0.987342,1,1,1,1,1,1,1,1,0.993711,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,0.993671,1,1,0.996835,1,1,0.996835,0.993671,1,0.949686,1,0.993671,1,1,1,1,1,1,0.993671,1,0.993671,0.974684,0.996835,0.993711,0.987421,0.996835,1,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.993671,0.996835,0.987421]
area_under_roc_curve
0.9964075312475119 [1,1,1,1,1,1,0.993711,1,0.996835,1,1,1,1,1,0.996835,1,1,0.990506,0.981132,0.955696,0.993711,1,0.987342,1,1,0.955696,0.996835,0.993711,1,1,1,1,1,0.987342,1,1,0.987421,0.955975,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.984177,1,1,1,0.974843,0.996835,0.996835,0.993671,1,0.987421,0.996835,0.993671,0.996835,1,1,0.993711,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.981132,1]
area_under_roc_curve
0.9956581880423533 [1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.90566,0.96519,1,1,1,1,0.974843,0.984177,0.981013,1,1,1,1,1,1,0.987342,1,1,1,0.949686,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.984177,1,1,0.993711,0.993671,1,1,1,1,1,0.993671,0.974684,1,1,0.990506,0.977848,0.993671,0.981132,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,0.962264,0.987342]
area_under_roc_curve
0.9962475618979381 [1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.943396,1,1,1,0.996835,1,0.990506,1,1,1,1,0.993671,0.993671,0.971519,1,1,1,1,1,1,0.974684,1,1,0.987342,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.981132,1,1,0.993671,0.993671,1,0.996835,1,0.993711,1,1,1,1,1,1,0.987421,1,0.996835,1,1,1,1,1,0.936709,1,0.996835,0.981013,1,1,1,1,1,0.996835,0.987421,1,1,1,1,1,1,0.996835,1,1,0.996835,0.993711,1,1,0.987421,1]
area_under_roc_curve
0.9962833870710931 [0.958861,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,0.949367,1,1,0.993671,1,0.993671,0.984177,0.993671,1,1,1,1,1,1,0.993671,1,1,0.993671,0.977848,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.968354,1,1,1,1,0.993671,1,0.987421,1,1,0.987342,1,1,1,1,1,0.987342,1,1,0.987342,1,1,0.993671,1,1,0.987421,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671]
area_under_roc_curve
0.995697993790303 [1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.984177,0.968354,0.996835,1,1,1,1,0.886792,0.918239,0.996835,1,1,0.996835,1,1,0.993711,1,1,0.974684,0.987342,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,1,0.993671,1,1,1,1,0.996835,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,0.933544,1]
area_under_roc_curve
0.9974347683305469 [1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,0.974684,1,1,1,1,1,0.993671,0.974843,0.993711,1,0.993711,1,1,1,1,0.993711,1,1,0.987342,0.990506,1,1,1,1,1,1,1,1,1,0.984177,1,1,0.974843,1,1,1,1,0.971519,1,1,0.993671,1,1,1,1,1,1,0.981132,0.949686,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1]
area_under_roc_curve
0.9969988953904944 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.981132,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.993711,1,1,0.996835,0.955696,1,1,1,1,1,0.993671,1,0.990506,1,1,0.993711,1,0.993671,1,1,0.936709,0.996835,0.949686,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.987421,0.974684,1,1,1,1,0.987421,1,0.996835,1,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1]
area_under_roc_curve
0.9977502288830508 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.981132,0.971519,0.968553,0.996835,1,1,1,1,0.993711,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,0.990506,0.987342,1,1,0.987342,1,0.993711,0.993711,0.996835,1,1,0.993711,1,1,0.990506,1,1,1,1,1,1,1,1,0.987421,1,0.993711,1,1,1,0.996835,1,1,0.993671,1,1,0.996835,1,1,0.993711,1,0.996835,1]
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
0.8294781716270625
kappa
0.8167673656359831
kappa
0.8610123983258312
kappa
0.7852518553608084
kappa
0.7915021323645554
kappa
0.8104639684106614
kappa
0.842047069973148
kappa
0.8357419252941641
kappa
0.8230857323381905
kappa
0.8105313018078472
kb_relative_information_score
98.94080858865034
kb_relative_information_score
97.23246302993917
kb_relative_information_score
98.4053391778786
kb_relative_information_score
97.18372452771322
kb_relative_information_score
97.23627284710444
kb_relative_information_score
96.04054050751265
kb_relative_information_score
100.75568685593909
kb_relative_information_score
98.66391732786633
kb_relative_information_score
98.90465551751473
kb_relative_information_score
98.53997282133689
mean_absolute_error
0.016066116524537318
mean_absolute_error
0.016291882357247642
mean_absolute_error
0.016162281687750885
mean_absolute_error
0.016266587715870046
mean_absolute_error
0.01630331444304117
mean_absolute_error
0.01642840642729982
mean_absolute_error
0.0158664633533644
mean_absolute_error
0.01614123271123941
mean_absolute_error
0.0160936170614776
mean_absolute_error
0.016153371707502855
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
0.83125
predictive_accuracy
0.81875
predictive_accuracy
0.8625
predictive_accuracy
0.7875
predictive_accuracy
0.79375
predictive_accuracy
0.8125
predictive_accuracy
0.84375
predictive_accuracy
0.8375
predictive_accuracy
0.825
predictive_accuracy
0.8125
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
0.83125 [1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1]
recall
0.81875 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,1,1,1,0.5,0.5,1,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0]
recall
0.8625 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1]
recall
0.7875 [1,1,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,0.5,0,0.5,1,1,0.5,0,0.5,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,0.5]
recall
0.79375 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,0,1,0,1,0.5,1,1,0,0,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,0,1]
recall
0.8125 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0,1,0,1,0.5,1,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5]
recall
0.84375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.8375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1]
recall
0.825 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0.5,0,0,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0.5,1]
recall
0.8125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
relative_absolute_error
0.8114200264917824
relative_absolute_error
0.8228223412751321
relative_absolute_error
0.81627685291671
relative_absolute_error
0.8215448341348496
relative_absolute_error
0.8233997193455121
relative_absolute_error
0.8297174963282724
relative_absolute_error
0.8013365329982008
relative_absolute_error
0.8152137732949184
relative_absolute_error
0.8128089424988673
relative_absolute_error
0.8158268539142842
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
0.08248120744432266
root_mean_squared_error
0.08338728748407379
root_mean_squared_error
0.08286440069273505
root_mean_squared_error
0.0834781168288494
root_mean_squared_error
0.0835657185559881
root_mean_squared_error
0.08427296586877404
root_mean_squared_error
0.08154791138420762
root_mean_squared_error
0.08273306369977088
root_mean_squared_error
0.08256220205259167
root_mean_squared_error
0.08289576014124318
root_relative_squared_error
0.8289673252978386
root_relative_squared_error
0.838073772333853
root_relative_squared_error
0.8328185623499033
root_relative_squared_error
0.8389866415962001
root_relative_squared_error
0.8398670720807643
root_relative_squared_error
0.8469751750216674
root_relative_squared_error
0.8195873469653582
root_relative_squared_error
0.8314985759051889
root_relative_squared_error
0.8297813517392632
root_relative_squared_error
0.8331337366660666
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
1390.4889670000002
usercpu_time_millis
1394.1362740000004
usercpu_time_millis
1398.472871
usercpu_time_millis
1396.0131499999998
usercpu_time_millis
1385.6656430000003
usercpu_time_millis
1404.9358539999996
usercpu_time_millis
1411.9353340000007
usercpu_time_millis
1412.917010000001
usercpu_time_millis
1393.9252470000001
usercpu_time_millis
1387.8165909999982
usercpu_time_millis_testing
157.98679900000013
usercpu_time_millis_testing
153.0356580000003
usercpu_time_millis_testing
153.36287099999967
usercpu_time_millis_testing
154.1039780000002
usercpu_time_millis_testing
153.03833499999973
usercpu_time_millis_testing
151.24827200000013
usercpu_time_millis_testing
162.6207940000004
usercpu_time_millis_testing
153.34652599999998
usercpu_time_millis_testing
154.35475600000004
usercpu_time_millis_testing
151.00122899999846
usercpu_time_millis_training
1232.502168
usercpu_time_millis_training
1241.1006160000002
usercpu_time_millis_training
1245.1100000000004
usercpu_time_millis_training
1241.9091719999997
usercpu_time_millis_training
1232.6273080000005
usercpu_time_millis_training
1253.6875819999996
usercpu_time_millis_training
1249.3145400000003
usercpu_time_millis_training
1259.570484000001
usercpu_time_millis_training
1239.5704910000002
usercpu_time_millis_training
1236.8153619999998