5987308
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)
4022041
bootstrap
false
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.5790955780545199
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
16
6902
min_samples_split
15
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
33398
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
false
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
12897905
description
https://api.openml.org/data/download/12897905/description.xml
-1
12897906
predictions
https://api.openml.org/data/download/12897906/predictions.arff
area_under_roc_curve
0.9895422979797976 [0.976405,0.997199,0.997869,0.999684,0.999408,0.994318,0.994673,0.995541,0.994476,0.999053,0.992424,0.995107,0.998619,0.97964,0.997159,0.993924,0.99929,0.978456,0.983862,0.974274,0.998422,0.997199,0.993016,0.999961,0.997909,0.954033,0.961845,0.994003,0.991162,0.998698,0.994634,0.995068,0.993095,0.983586,0.998185,0.992661,0.987216,0.975221,0.992227,0.995739,0.998422,0.997909,0.996567,0.991832,0.995028,0.998974,0.99345,0.995502,0.99345,0.9869,0.990807,0.988242,0.991398,0.982757,0.991793,0.973879,0.999527,0.992069,0.985953,0.961411,0.999763,0.997317,0.985756,0.98619,0.994003,0.99345,0.974511,0.986269,0.944878,0.97968,0.971828,0.99712,0.984296,0.995423,0.960188,0.988755,0.997751,0.97242,0.997672,0.995344,0.983783,0.988755,0.994555,0.993766,0.992148,0.994121,0.999448,0.984336,0.994003,0.999053,0.997869,0.996843,0.993529,0.979127,0.987768,0.994279,0.991872,0.986742,0.962397,0.996094]
average_cost
0
kappa
0.6029040404040404
kb_relative_information_score
1051.4886779014328
mean_absolute_error
0.014612595608359813
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]
predictive_accuracy
0.606875
prior_entropy
6.6438561897747395
recall
0.606875 [0.3125,0.875,0.875,0.9375,1,0.5,0.8125,0.5625,0.75,1,0.6875,0.875,0.8125,0.375,0.875,0.625,0.9375,0.4375,0.1875,0.0625,0.875,0.4375,0.4375,1,0.75,0.0625,0.0625,0.4375,0.75,0.8125,0.8125,0.875,0.875,0.4375,0.8125,0.625,0.5,0.3125,0.4375,0.75,0.9375,0.875,0.8125,0.4375,0.5625,0.9375,0.875,0.75,0.75,0.6875,0.75,0.5625,0.625,0.1875,0.625,0.25,1,0.6875,0.0625,0.375,1,1,0.5,0.75,0.75,0.8125,0.375,0.3125,0,0.25,0,0.625,0.5625,0.6875,0.125,0.0625,0.875,0.5,0.9375,0.375,0.125,0.5625,0.5,0.6875,0.6875,0.625,0.875,0.5625,0.75,0.9375,0.875,0.75,0.6875,0.25,0.5625,0.75,0.5625,0.125,0.1875,0.8125]
relative_absolute_error
0.7380098792100902
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08009055500786894
root_relative_squared_error
0.8049403642800627
total_cost
0
area_under_roc_curve
0.988376721598599 [1,0.993671,1,1,1,1,1,1,0.981013,1,0.971519,0.996835,0.993671,0.971519,1,0.987421,0.996835,0.971519,0.968354,0.968553,0.987421,1,0.987421,1,0.993711,0.990506,0.905063,1,0.990506,1,0.952532,0.996835,0.974684,0.96519,1,0.981132,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.955696,0.993671,1,0.987421,0.984177,1,1,1,1,0.987421,0.981013,1,1,1,0.974684,1,1,0.981013,0.962264,1,1,0.971519,1,0.958861,1,1,0.996835,1,0.977848,1,1,0.993671,0.996835,0.993711,1,0.984177,0.990506,1,1,0.984177,1,0.996835,1,0.993671,0.987342,1,0.981013,1,0.996835,0.857595,1]
area_under_roc_curve
0.9871992178170526 [0.993711,1,1,1,1,0.993711,1,1,1,1,1,0.993671,1,0.96519,1,1,0.996835,0.946203,0.990506,0.987421,1,1,1,1,1,0.936709,0.981013,0.974684,1,1,1,1,1,0.990506,0.996835,0.974843,0.968553,1,0.987421,1,1,1,1,0.971519,1,1,1,1,0.990506,0.996835,1,0.958861,1,1,0.993711,0.962264,1,1,1,0.844937,1,0.987421,1,0.958861,0.993711,0.990506,0.993671,1,0.786164,0.993711,0.949367,1,0.996835,0.993671,1,0.996835,0.987342,0.927215,1,0.987342,0.968354,0.987342,0.987421,0.993711,0.977848,1,1,0.996835,1,1,0.987342,1,1,0.984177,1,1,1,0.96519,0.990506,1]
area_under_roc_curve
0.990515285407213 [1,1,1,1,1,0.996835,0.993711,1,0.990506,1,1,1,1,0.993671,1,1,1,0.987342,0.974843,0.911392,1,1,0.981013,1,1,0.993671,1,0.993711,1,1,0.993711,1,1,0.974684,1,1,0.993711,0.930818,1,1,0.990506,1,1,1,0.987342,1,0.981013,1,0.977848,1,1,1,1,1,1,0.993671,1,1,0.962264,0.914557,1,0.987421,0.993671,1,0.987342,0.984177,0.984177,0.974684,0.981132,0.955696,0.962025,0.996835,1,1,1,1,1,0.924051,0.993671,1,0.993711,1,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.974684,1,1,0.996835,0.977848,0.968553,0.990506]
area_under_roc_curve
0.9924538253323781 [0.958861,1,1,1,1,0.993671,1,1,0.996835,1,0.993671,1,1,0.990506,1,1,1,1,0.949686,0.981013,1,1,1,1,0.987421,0.974684,0.93038,0.993711,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,0.993671,0.981013,0.993671,1,0.937107,0.946203,1,0.981013,1,0.974684,1,0.993671,1,1,0.990506,1,0.990506,1,0.96519,0.993671,0.993711,0.984177,0.987342,1,0.987421,1,0.987421,0.984177,1,0.984177,1,1,0.955975,0.990506,1,0.996835,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.993711,0.993671,0.993671,0.993711,0.993671]
area_under_roc_curve
0.9875529914019582 [0.977848,1,1,1,1,0.996835,1,0.996835,1,1,0.981013,0.968354,1,0.886792,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.952532,0.981013,1,1,0.996835,1,0.937107,1,0.968354,1,1,0.987342,0.905063,0.990506,1,1,1,1,1,1,1,1,0.996835,0.990506,1,1,0.968553,0.996835,0.993711,0.974684,0.968354,1,0.990506,0.993671,0.987421,1,1,0.993711,1,0.987342,1,0.949686,0.968354,0.908228,0.971519,0.990506,1,1,1,0.882911,1,1,0.981013,1,1,1,1,0.987342,0.993671,0.981132,0.974843,1,1,1,1,1,0.987342,1,1,0.93038,1,0.993671,1,0.987421,0.993671]
area_under_roc_curve
0.9927680419552585 [0.968354,1,1,1,1,0.987342,1,0.993671,1,1,0.996835,1,1,0.993711,1,1,1,0.987342,1,0.955696,1,1,0.996835,1,1,0.987342,0.974684,1,1,1,1,1,0.968354,1,1,0.996835,0.987342,0.990506,0.990506,0.987342,1,0.993671,1,0.987421,0.993711,1,1,0.993671,1,0.996835,1,1,0.990506,1,0.996835,0.987342,1,1,0.987342,1,1,1,0.987421,1,1,1,1,1,0.958861,0.993671,0.987342,0.990506,1,1,0.93038,0.968553,0.996835,0.990506,1,1,0.993711,0.984177,1,0.996835,0.993711,1,1,0.993711,0.974684,1,1,0.993671,0.937107,1,1,1,1,0.981132,0.993711,0.996835]
area_under_roc_curve
0.9932183444789426 [1,0.993711,0.987421,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,0.958861,1,1,0.993671,0.981013,0.996835,0.996835,0.996835,1,0.990506,0.962264,0.937107,0.993671,1,0.996835,1,0.993711,1,0.962264,1,1,0.993671,0.996835,1,1,1,1,1,0.996835,0.968553,0.993671,0.977848,0.990506,1,1,0.899371,1,1,0.993711,0.990506,1,1,0.996835,0.987342,0.993711,1,1,1,0.996835,0.996835,1,0.968553,0.993671,0.987342,0.984177,1,1,1,1,1,0.987421,1,1,1,1,0.981013,1,1,1,1,0.996835,1,0.918239,1,1,1,1,0.987342,0.91195,0.974843,1,1,0.993711,0.990506,1]
area_under_roc_curve
0.9901729559748425 [0.993671,1,1,0.996835,1,0.996835,1,1,0.993711,1,0.987421,1,1,1,0.981132,0.996835,1,0.993711,0.981013,0.993671,1,1,1,1,1,0.955975,0.981132,1,0.91195,0.996835,1,1,1,1,1,1,0.990506,0.971519,0.996835,1,1,1,1,0.996835,0.993711,0.996835,1,1,0.993711,1,0.993711,0.993671,0.977848,1,0.996835,0.927215,1,1,0.974684,0.968553,1,0.993671,0.993711,1,1,0.943396,1,0.987342,0.927215,0.971519,0.962264,0.996835,0.993671,1,0.984177,1,1,0.993711,1,0.993711,0.974684,0.962264,0.984177,1,0.993671,0.984177,1,0.924528,1,1,1,0.996835,1,0.924528,1,0.984177,0.981013,0.974843,0.971519,0.996835]
area_under_roc_curve
0.9903281983918477 [0.955975,0.996835,1,1,0.996835,0.993711,0.968354,0.981132,0.996835,1,0.993711,1,1,1,0.996835,0.993711,1,1,1,1,1,0.993671,0.955975,1,1,0.855346,0.981132,0.996835,1,1,0.996835,1,1,1,1,1,1,0.962025,0.993671,0.974843,1,0.987342,0.996835,0.990506,1,1,1,1,0.987421,1,1,0.977848,1,0.968354,0.993671,0.949686,1,0.993711,1,0.977848,1,1,1,0.990506,1,1,0.968354,1,0.958861,0.993711,1,0.987421,0.96519,0.974684,0.96519,0.974684,1,0.993711,1,0.996835,0.987342,0.962264,1,0.993711,1,0.996835,1,0.981013,0.981132,1,1,1,1,0.977848,0.981132,0.987342,0.993711,0.996835,0.962025,1]
area_under_roc_curve
0.9908504000477667 [0.874214,1,0.968553,1,1,0.993711,1,0.974843,0.996835,1,0.993711,1,1,0.984177,1,0.987421,1,0.962264,0.993671,1,1,1,1,1,1,0.867925,0.993711,0.996835,0.993671,1,1,1,1,0.993711,0.993671,0.993671,0.987342,0.990506,0.987342,1,0.993711,1,1,0.996835,1,1,1,0.987342,0.993711,0.993711,0.981013,0.996835,0.993671,0.977848,1,0.955975,1,0.981132,0.993671,0.993671,1,1,0.968354,0.981013,1,1,0.990506,0.993711,0.943038,0.974843,0.90566,1,0.984177,1,0.962025,0.990506,1,0.993711,1,0.993671,1,0.987421,0.993711,0.968553,0.996835,1,0.993671,0.987342,1,0.996835,1,0.996835,0.993671,1,1,1,0.955975,1,1,1]
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
0.5768532407041921
kappa
0.6526681401957689
kappa
0.6210925165772024
kappa
0.6336793905182962
kappa
0.5830701200252685
kappa
0.6210925165772024
kappa
0.6147165640296858
kappa
0.6272752398625973
kappa
0.5642563940637828
kappa
0.5327545382794001
kb_relative_information_score
103.62785974698733
kb_relative_information_score
105.31669508168201
kb_relative_information_score
105.33599928551001
kb_relative_information_score
105.85812333791837
kb_relative_information_score
104.12870495777399
kb_relative_information_score
106.74120902689782
kb_relative_information_score
107.54066853694938
kb_relative_information_score
105.23429920293842
kb_relative_information_score
104.32488397810546
kb_relative_information_score
103.38023474667195
mean_absolute_error
0.014715414446463626
mean_absolute_error
0.014430208169602873
mean_absolute_error
0.014572816500257286
mean_absolute_error
0.014698709305632506
mean_absolute_error
0.014526503940923405
mean_absolute_error
0.014574561415733805
mean_absolute_error
0.014378555791445785
mean_absolute_error
0.014588130975477115
mean_absolute_error
0.014785218837587655
mean_absolute_error
0.014855836700473992
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
0.58125
predictive_accuracy
0.65625
predictive_accuracy
0.625
predictive_accuracy
0.6375
predictive_accuracy
0.5875
predictive_accuracy
0.625
predictive_accuracy
0.61875
predictive_accuracy
0.63125
predictive_accuracy
0.56875
predictive_accuracy
0.5375
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
0.58125 [1,1,1,1,1,1,1,1,0,1,0,0.5,0.5,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0.5,0,0.5,1,0.5,0,1,0,1,1,1,0.5,1,1,1,0,0.5,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,0,0,1,1,0.5,0.5,1,1,0.5,0,0,1,0,0,0,1,0,0,0.5,0.5,1,0.5,0,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,0,0,1]
recall
0.65625 [1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,0,0,1,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,0,0,1,1,0,0.5,1,1,1,0.5,1,0,0.5,1,0,0,0,1,1,0.5,0,0,0.5,0.5,1,0,0,0.5,0,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0.5,0.5,1]
recall
0.625 [0,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,0.5,1,1,0,1,0,1,1,0,0.5,1,1,0.5,1,0.5,1,0.5,0,0,0,0,1,0,0.5,0,0.5,1,0.5,0.5,0,0,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0,0,0.5]
recall
0.6375 [0,1,1,1,1,0,1,0.5,0.5,1,0.5,1,1,0,1,1,1,1,0,0,1,1,1,1,0,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0,1,0,1,0.5,0,0.5,1,1,0.5,1,0.5,1,0,0.5,0,0,0,0.5,1,0.5,0,0,1,0.5,1,0.5,0,0.5,1,1,1,0,1,1,1,1,1,1,0,0.5,1,1,0,0,0,0.5]
recall
0.5875 [0,1,1,1,1,1,1,0.5,0,1,0.5,0.5,0,0,1,1,1,0.5,0,0,1,0.5,0.5,1,0.5,0,0,0,1,0.5,1,0,1,0.5,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,0.5,0,1,0.5,0,1,1,1,0,1,0.5,1,0,0,0,0.5,0,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0,1,0,1,1,1,0,1,0,0,1,0.5,0,0,0.5]
recall
0.625 [0,1,1,1,1,0.5,1,0,1,1,1,1,0,1,1,1,1,0.5,0,0,1,0.5,0.5,1,1,0,0,0,0,1,0,1,0.5,1,1,0.5,0.5,0.5,0,0,1,0.5,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0,1,1,1,1,1,0,0,0,0,1,1,0.5,0,1,0,1,1,0,0.5,0.5,0.5,0,1,1,1,0,1,1,1,0,1,0.5,1,1,0,0,1]
recall
0.61875 [0.5,0,0,0.5,1,0,0.5,1,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,0,0,1,0.5,0,0,0.5,0,1,0.5,0,1,1,1,1,0,0.5,0.5,1,1,1,1,0.5,0,0.5,0.5,1,1,1,0,1,1,0,0.5,0.5,1,0.5,0,0,1,1,1,1,0.5,1,0,0.5,0,0.5,0,1,1,1,0,0,1,1,1,1,0,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,0,0,0.5,1,1,0,1]
recall
0.63125 [1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,0,0,0.5,1,1,0,0,0.5,0.5,0.5,1,1,0,0,0.5,0,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,0,1,1,1,1,1,0,0.5,0.5,1,1,0.5,1,1,0.5,0,1,1,0,1,1,0,1,0,0,0,0,1,0.5,1,0.5,0,1,1,1,1,0,0,0.5,1,0,0.5,1,0,1,1,0,0.5,1,0,1,0.5,0,0,0,1]
recall
0.56875 [0,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,0,1,1,0.5,0,1,0.5,0,1,1,0,0,1,1,1,1,1,1,1,0.5,0.5,0,0,0.5,0,1,0.5,0.5,0.5,0,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,0,0,1,1,1,0.5,1,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,1,1,0.5,0,1,1,1,0.5,0,0,0.5,1,0,0,1]
recall
0.5375 [0,0.5,0,1,1,1,1,0,1,1,1,1,1,0.5,0.5,0,1,0,0,0,1,0.5,1,1,1,0,0,0,1,1,1,1,1,1,0.5,0,0.5,0,0,1,1,1,0.5,0,0.5,1,1,0.5,1,1,0.5,0.5,0,0,0.5,0,1,0,0,0.5,1,1,0,0.5,1,1,0.5,0,0,0,0,0,0.5,1,0,0,1,1,1,0,0.5,0,1,0,1,0.5,0,0,1,0.5,1,0.5,0.5,0,0,1,1,0,1,1]
relative_absolute_error
0.7432027498213941
relative_absolute_error
0.7287983924041843
relative_absolute_error
0.7360008333463264
relative_absolute_error
0.7423590558400244
relative_absolute_error
0.7336618151981505
relative_absolute_error
0.736088960390595
relative_absolute_error
0.7261896864366545
relative_absolute_error
0.7367742916907623
relative_absolute_error
0.7467282241205874
relative_absolute_error
0.7502947828522205
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
0.08100591516257748
root_mean_squared_error
0.07920106111379754
root_mean_squared_error
0.0796660463665645
root_mean_squared_error
0.08025269358644253
root_mean_squared_error
0.08009786736236839
root_mean_squared_error
0.07955570350763826
root_mean_squared_error
0.07888831596427129
root_mean_squared_error
0.07959867371632416
root_mean_squared_error
0.08090187442101399
root_mean_squared_error
0.08169346984761178
root_relative_squared_error
0.8141400799806989
root_relative_squared_error
0.7960006142802237
root_relative_squared_error
0.8006738919059103
root_relative_squared_error
0.8065699183078513
root_relative_squared_error
0.8050138562079683
root_relative_squared_error
0.7995649044472635
root_relative_squared_error
0.7928574072620959
root_relative_squared_error
0.7999967712938526
root_relative_squared_error
0.8130944311847098
root_relative_squared_error
0.82105026456588
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
2445.575469000005
usercpu_time_millis
2328.117020999997
usercpu_time_millis
2415.2841220000173
usercpu_time_millis
2313.7862129999858
usercpu_time_millis
2353.1524410000056
usercpu_time_millis
2359.375531000012
usercpu_time_millis
2336.5916119999924
usercpu_time_millis
2315.3954010000034
usercpu_time_millis
2317.155005999993
usercpu_time_millis
2341.561685000002
usercpu_time_millis_testing
39.475471000002926
usercpu_time_millis_testing
33.71020299999827
usercpu_time_millis_testing
32.55555600000548
usercpu_time_millis_testing
31.847697999992874
usercpu_time_millis_testing
32.743216000000075
usercpu_time_millis_testing
32.20671900000127
usercpu_time_millis_testing
32.51347299999452
usercpu_time_millis_testing
32.228699000000915
usercpu_time_millis_testing
31.981418000000872
usercpu_time_millis_testing
32.11845399999902
usercpu_time_millis_training
2406.099998000002
usercpu_time_millis_training
2294.4068179999986
usercpu_time_millis_training
2382.7285660000116
usercpu_time_millis_training
2281.938514999993
usercpu_time_millis_training
2320.409225000006
usercpu_time_millis_training
2327.168812000011
usercpu_time_millis_training
2304.078138999998
usercpu_time_millis_training
2283.1667020000027
usercpu_time_millis_training
2285.1735879999924
usercpu_time_millis_training
2309.443231000003