6026025
1
Jan van Rijn
9954
Supervised Classification
6969
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1)
4060710
bootstrap
true
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.4572912158997313
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
2
6902
min_samples_split
12
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
7901
6902
verbose
0
6902
warm_start
false
6902
axis
0
6947
categorical_features
[]
6947
copy
true
6947
fill_empty
0
6947
missing_values
"NaN"
6947
strategy
"median"
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
true
6948
threshold
0.0
6949
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
12975161
description
https://api.openml.org/data/download/12975161/description.xml
-1
12975162
predictions
https://api.openml.org/data/download/12975162/predictions.arff
area_under_roc_curve
0.9959935290404042 [0.99562,0.999724,0.999921,1,0.999684,0.996922,0.999605,0.999803,0.998106,0.999803,0.999014,0.999921,0.999684,0.99345,1,0.999842,1,0.982797,0.990964,0.975852,0.999724,0.999882,0.996449,1,0.998777,0.968711,0.984336,0.998856,0.998264,0.999961,0.999842,0.999921,0.998974,0.9884,0.999803,0.997475,0.994437,0.989465,0.999487,0.999842,0.999645,0.999487,0.999053,0.998303,0.998737,0.999842,0.998698,0.99925,0.996843,0.997475,0.998303,0.993687,0.996015,0.985085,0.997435,0.98035,0.999961,0.999171,0.994003,0.991241,1,0.999211,0.993766,0.998382,0.998264,0.996488,0.980824,0.999763,0.988242,0.99566,0.995699,0.99854,0.995818,1,0.973366,0.999487,0.999724,0.992898,0.999763,0.999527,0.995975,0.997593,0.99858,0.99708,0.996804,0.997277,0.999961,0.993766,0.998816,1,0.999645,0.999053,0.997869,0.992977,0.998422,0.999092,0.998224,0.996449,0.970683,0.999763]
average_cost
0
f_measure
0.8000551166432888 [0.642857,0.833333,0.967742,1,0.967742,0.785714,0.909091,0.969697,0.875,0.9375,0.909091,0.914286,0.909091,0.758621,1,0.933333,0.914286,0.514286,0.588235,0.424242,0.866667,0.9375,0.774194,1,0.823529,0.363636,0.275862,0.83871,0.848485,0.969697,0.9375,0.866667,0.875,0.5,0.941176,0.756757,0.5,0.444444,0.848485,0.9375,0.833333,0.969697,0.777778,0.83871,0.9375,0.909091,0.83871,0.896552,0.6875,0.8,0.848485,0.611111,0.787879,0.666667,0.787879,0.516129,0.969697,0.866667,0.545455,0.709677,1,0.8,0.727273,0.914286,0.882353,0.810811,0.30303,0.914286,0.56,0.666667,0.733333,0.866667,0.647059,0.967742,0.64,0.875,0.9375,0.608696,0.969697,0.903226,0.740741,0.774194,0.787879,0.666667,0.666667,0.875,1,0.742857,0.848485,1,0.864865,0.882353,0.875,0.769231,0.9375,0.857143,0.909091,0.774194,0.538462,0.909091]
kappa
0.8017676767676767
kb_relative_information_score
1164.8371715524504
mean_absolute_error
0.013117729901332819
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
0.8107639014752014 [0.75,0.75,1,1,1,0.916667,0.882353,0.941176,0.875,0.9375,0.882353,0.842105,0.882353,0.846154,1,1,0.842105,0.473684,0.555556,0.411765,0.928571,0.9375,0.8,1,0.777778,0.666667,0.307692,0.866667,0.823529,0.941176,0.9375,0.928571,0.875,0.5,0.888889,0.666667,0.5,0.4,0.823529,0.9375,0.75,0.941176,0.7,0.866667,0.9375,0.882353,0.866667,1,0.6875,0.857143,0.823529,0.55,0.764706,0.818182,0.764706,0.533333,0.941176,0.928571,0.529412,0.733333,1,0.666667,0.705882,0.842105,0.833333,0.714286,0.294118,0.842105,0.777778,0.714286,0.785714,0.928571,0.611111,1,0.888889,0.875,0.9375,1,0.941176,0.933333,0.909091,0.8,0.764706,0.647059,0.647059,0.875,1,0.684211,0.823529,1,0.761905,0.833333,0.875,1,0.9375,0.789474,0.882353,0.8,0.7,0.882353]
predictive_accuracy
0.80375
prior_entropy
6.6438561897747395
recall
0.80375 [0.5625,0.9375,0.9375,1,0.9375,0.6875,0.9375,1,0.875,0.9375,0.9375,1,0.9375,0.6875,1,0.875,1,0.5625,0.625,0.4375,0.8125,0.9375,0.75,1,0.875,0.25,0.25,0.8125,0.875,1,0.9375,0.8125,0.875,0.5,1,0.875,0.5,0.5,0.875,0.9375,0.9375,1,0.875,0.8125,0.9375,0.9375,0.8125,0.8125,0.6875,0.75,0.875,0.6875,0.8125,0.5625,0.8125,0.5,1,0.8125,0.5625,0.6875,1,1,0.75,1,0.9375,0.9375,0.3125,1,0.4375,0.625,0.6875,0.8125,0.6875,0.9375,0.5,0.875,0.9375,0.4375,1,0.875,0.625,0.75,0.8125,0.6875,0.6875,0.875,1,0.8125,0.875,1,1,0.9375,0.875,0.625,0.9375,0.9375,0.9375,0.75,0.4375,0.9375]
relative_absolute_error
0.662511611178424
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07183071307667165
root_relative_squared_error
0.7219258293908932
total_cost
0
area_under_roc_curve
0.9956106699307379 [0.993711,0.993671,1,1,1,1,1,1,1,1,0.996835,0.996835,1,0.993671,1,1,1,0.993671,0.974684,1,1,1,0.993711,1,1,0.993671,0.96519,1,1,1,0.996835,1,0.996835,0.987342,1,0.987421,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.971519,0.996835,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.977848,1,1,0.993711,0.984177,1,0.996835,1,1,1,1,0.993671,1,1,1,1,1,1,0.990506,0.987342,1,0.996835,0.996835,1,1,1,1,0.993671,1,0.990506,1,0.990506,0.917722,1]
area_under_roc_curve
0.9954942381179842 [0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.977848,1,1,1,0.914557,0.996835,0.987421,1,1,1,1,1,0.96519,0.990506,1,1,1,1,1,1,1,1,0.993711,0.981132,1,1,1,1,1,1,0.987342,1,1,1,1,0.993671,1,1,0.974684,1,1,1,0.955975,1,1,1,0.962025,1,1,1,0.993671,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.977848,1,1,1,0.990506,0.990506,1]
area_under_roc_curve
0.9971969289865455 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.958861,1,1,0.984177,1,1,0.990506,1,1,1,1,1,1,1,0.981013,1,1,0.993711,0.949686,1,1,0.996835,1,1,1,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.981132,0.987342,1,1,1,1,0.996835,0.996835,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.987342,1,1,1,0.993671,0.955975,1]
area_under_roc_curve
0.9967615536183423 [0.996835,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.943396,0.990506,1,1,1,1,0.993711,0.987342,0.981013,1,1,1,1,1,1,0.987342,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,0.993671,1,1,1,1,1,1,0.996835,1,0.996835,1,0.943038,0.993671,1,1,0.990506,0.996835,0.987421,1,0.993711,1,1,1,1,1,0.993711,1,1,1,0.987421,1,1,1,1,1,1,1,0.987421,1,1,0.993711,0.987342,1,1,0.996835]
area_under_roc_curve
0.9948605803678049 [0.996835,1,1,1,1,0.996835,1,0.996835,1,1,0.996835,1,1,0.993711,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.971519,1,1,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.981013,1,0.984177,0.974684,1,0.996835,1,1,1,1,1,1,0.996835,1,0.968553,1,0.993671,0.993671,1,1,1,1,0.867089,1,1,0.984177,1,1,1,1,1,0.993671,0.987421,1,1,1,1,1,1,0.993671,1,1,0.984177,1,0.996835,1,1,1]
area_under_roc_curve
0.99608684619059 [0.996835,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.905063,1,1,1,1,1,1,0.96519,1,1,1,1,1,0.993671,0.996835,1,0.993671,0.977848,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,0.987421,1,1,0.996835,1,1,0.971519,1,1,0.993671,1,1,0.981013,1,1,1,1,1,0.993711,0.993671,1,0.993671,1,1,1,0.993711,1,1,1,1,0.993711,1,1,1,1,0.993711,0.962264,1]
area_under_roc_curve
0.9962535327601302 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.996835,1,0.996835,0.861635,0.981132,0.993671,1,1,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.993671,0.996835,0.993711,1,0.993711,1,1,1,0.996835,0.984177,1,1,1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.937107,1,1,1,1,0.990506,1,1,1,1,1,0.936709,1]
area_under_roc_curve
0.9963284173234616 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671,0.981013,1,1,1,1,1,1,0.993711,1,0.968553,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993711,0.996835,0.990506,1,0.996835,0.939873,1,1,0.987342,0.987421,1,0.996835,1,1,1,0.962264,0.949686,1,0.977848,0.987342,1,1,1,1,0.993671,1,1,0.987421,1,1,0.987342,1,0.990506,1,1,0.993671,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1]
area_under_roc_curve
0.9965279436350608 [0.993711,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.962264,1,1,0.930818,0.981132,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,0.996835,0.993671,1,0.949367,1,0.968553,1,0.993711,1,0.990506,1,1,0.996835,1,1,1,0.993671,1,0.993671,0.993711,1,0.987421,0.987342,1,1,1,1,0.987421,1,1,0.996835,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.984177,1,1,0.993711,1,0.987342,1]
area_under_roc_curve
0.996449078496935 [0.981132,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993711,0.993671,0.993711,1,1,1,1,1,0.880503,0.993711,1,0.993671,1,1,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.974684,1,1,1,1,1,0.996835,1,0.996835,0.974684,1,0.993711,1,0.993671,1,0.990506,0.993711,1,1,0.984177,1,0.977848,1,1,1,1,0.993671,1,0.981132,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.7789008212255212
kappa
0.8420657796027955
kappa
0.8294243070362474
kappa
0.753632343651295
kappa
0.7978681405448086
kappa
0.7978202495656295
kappa
0.8293838862559242
kappa
0.7662204320183233
kappa
0.8041151613285416
kappa
0.8167528928557324
kb_relative_information_score
115.50698279444363
kb_relative_information_score
115.62219974189674
kb_relative_information_score
117.04461908874256
kb_relative_information_score
115.97884886263233
kb_relative_information_score
116.50106637826117
kb_relative_information_score
115.7293491157829
kb_relative_information_score
118.22828055921646
kb_relative_information_score
117.0137775538688
kb_relative_information_score
116.3788639694972
kb_relative_information_score
116.83318348811007
mean_absolute_error
0.013168044533344397
mean_absolute_error
0.013259377205221076
mean_absolute_error
0.013094189363406502
mean_absolute_error
0.013304238569448342
mean_absolute_error
0.013022758311286301
mean_absolute_error
0.013311342442842596
mean_absolute_error
0.012761927860398278
mean_absolute_error
0.012955203323754882
mean_absolute_error
0.01318596575640168
mean_absolute_error
0.013114251647224092
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
0.78125
predictive_accuracy
0.84375
predictive_accuracy
0.83125
predictive_accuracy
0.75625
predictive_accuracy
0.8
predictive_accuracy
0.8
predictive_accuracy
0.83125
predictive_accuracy
0.76875
predictive_accuracy
0.80625
predictive_accuracy
0.81875
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
0.78125 [1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,0,0.5,1,1,1,0,1,0,0.5,0,1,1,1,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,1]
recall
0.84375 [1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
recall
0.83125 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1]
recall
0.75625 [0,1,1,1,0.5,0,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0,0,0,1,1,1,0.5,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,0,0.5,0,0.5,0,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,0.5]
recall
0.8 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1]
recall
0.8 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1]
recall
0.83125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0,0.5,1,1,0.5,0,1,0,1,1,0.5,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,0,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.76875 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,0,0,1,0,0.5,0,1,1,1,1,1,1,0,1,1,0,0,0,1,0,0.5,1,0,1,1,1,1,1,0,1,1,1,0,0.5,1]
recall
0.80625 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,0.5,0.5,1,0,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,1]
recall
0.81875 [0,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0,1,1,0,1,0.5,1,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.6650527542093119
relative_absolute_error
0.6696655154152047
relative_absolute_error
0.6613226951215394
relative_absolute_error
0.6719312408812282
relative_absolute_error
0.6577150662265798
relative_absolute_error
0.6722900223657865
relative_absolute_error
0.6445418111312251
relative_absolute_error
0.6543031981694374
relative_absolute_error
0.6659578664849323
relative_absolute_error
0.6623359417789935
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
0.07232153722007122
root_mean_squared_error
0.07218092342472976
root_mean_squared_error
0.07142631720292085
root_mean_squared_error
0.07280365507191079
root_mean_squared_error
0.07145683353944757
root_mean_squared_error
0.07266145104062507
root_mean_squared_error
0.07022419005401966
root_mean_squared_error
0.07112561486302574
root_mean_squared_error
0.07204885571407584
root_mean_squared_error
0.0720197117656038
root_relative_squared_error
0.7268587976384812
root_relative_squared_error
0.7254455758218283
root_relative_squared_error
0.71786149793635
root_relative_squared_error
0.7317042643635843
root_relative_squared_error
0.7181681986582753
root_relative_squared_error
0.7302750600743398
root_relative_squared_error
0.7057796655023956
root_relative_squared_error
0.7148393257090349
root_relative_squared_error
0.7241182453880093
root_relative_squared_error
0.7238253376850057
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
2231.588297999224
usercpu_time_millis
2091.4554110004246
usercpu_time_millis
2090.8890470000188
usercpu_time_millis
2084.7172670000873
usercpu_time_millis
2343.160542000078
usercpu_time_millis
2098.012577999725
usercpu_time_millis
2384.6524769996904
usercpu_time_millis
2199.8222539996277
usercpu_time_millis
2088.6265330009337
usercpu_time_millis
2099.315477998971
usercpu_time_millis_testing
34.042399999634654
usercpu_time_millis_testing
33.84005800035084
usercpu_time_millis_testing
34.72604699982185
usercpu_time_millis_testing
34.84720100004779
usercpu_time_millis_testing
33.95912399992085
usercpu_time_millis_testing
33.82897200026491
usercpu_time_millis_testing
43.177094000384386
usercpu_time_millis_testing
33.996528999523434
usercpu_time_millis_testing
34.24534900022991
usercpu_time_millis_testing
33.547451999766054
usercpu_time_millis_training
2197.545897999589
usercpu_time_millis_training
2057.615353000074
usercpu_time_millis_training
2056.163000000197
usercpu_time_millis_training
2049.8700660000395
usercpu_time_millis_training
2309.201418000157
usercpu_time_millis_training
2064.1836059994603
usercpu_time_millis_training
2341.475382999306
usercpu_time_millis_training
2165.8257250001043
usercpu_time_millis_training
2054.381184000704
usercpu_time_millis_training
2065.768025999205