6008538
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)
4043239
bootstrap
false
6902
class_weight
null
6902
criterion
"gini"
6902
max_depth
null
6902
max_features
0.6540382807559095
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max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
18
6902
min_samples_split
4
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
58544
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
"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"
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n_values
"auto"
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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
12940211
description
https://api.openml.org/data/download/12940211/description.xml
-1
12940212
predictions
https://api.openml.org/data/download/12940212/predictions.arff
area_under_roc_curve
0.9870432844065659 [0.972735,0.995344,0.99637,0.99925,0.998856,0.992148,0.994515,0.992306,0.992937,0.99854,0.989781,0.995226,0.997514,0.969658,0.994397,0.990451,0.99858,0.972262,0.977115,0.969697,0.998737,0.996173,0.991556,0.999724,0.994831,0.951034,0.965002,0.98836,0.992109,0.996291,0.993687,0.994121,0.990175,0.983941,0.996291,0.992622,0.985243,0.971236,0.990491,0.995265,0.996646,0.995423,0.995975,0.989702,0.99199,0.99779,0.990412,0.993095,0.98982,0.986979,0.990017,0.988636,0.989504,0.977115,0.986229,0.975379,0.998856,0.980784,0.985401,0.96583,0.999566,0.995423,0.949179,0.99057,0.991911,0.992582,0.968789,0.979877,0.947956,0.976444,0.965554,0.996469,0.980784,0.995068,0.958767,0.986427,0.995739,0.969697,0.997554,0.992109,0.975931,0.987532,0.99349,0.994081,0.989307,0.992266,0.999171,0.983744,0.992819,0.998382,0.994673,0.995739,0.986466,0.972696,0.981574,0.990885,0.987808,0.981889,0.961569,0.993687]
average_cost
0
kappa
0.5492424242424242
kb_relative_information_score
1016.0112739711581
mean_absolute_error
0.015121903500502696
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
0.55375
prior_entropy
6.6438561897747395
recall
0.55375 [0.25,0.8125,0.875,0.875,0.9375,0.375,0.6875,0.5,0.5625,0.9375,0.5,0.875,0.875,0.125,0.75,0.5625,0.9375,0.4375,0.1875,0.125,0.9375,0.5,0.375,1,0.625,0,0.0625,0.3125,0.75,0.6875,0.75,0.875,0.75,0.4375,0.75,0.5625,0.625,0.375,0.3125,0.75,0.8125,0.8125,0.8125,0.3125,0.375,0.875,0.8125,0.5625,0.6875,0.75,0.75,0.625,0.5625,0.125,0.75,0.125,0.875,0.6875,0,0.3125,1,0.9375,0.5,0.75,0.6875,0.6875,0.1875,0.1875,0,0.1875,0,0.5625,0.4375,0.625,0.25,0,0.875,0.125,0.9375,0.3125,0.0625,0.625,0.5,0.8125,0.625,0.5625,0.875,0.5625,0.6875,0.875,0.6875,0.75,0.3125,0.0625,0.5,0.625,0.4375,0.125,0.25,0.9375]
relative_absolute_error
0.7637325000253873
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08242970200958241
root_relative_squared_error
0.8284496762017852
total_cost
0
area_under_roc_curve
0.9853333233818965 [1,0.987342,1,1,0.990506,1,1,1,0.974684,0.993711,0.977848,0.993671,0.987342,0.927215,0.993671,0.974843,1,0.955696,0.955696,0.968553,1,1,0.987421,1,0.993711,0.990506,0.93038,1,0.981013,1,0.933544,0.993671,0.962025,0.96519,1,0.987421,1,0.993711,1,1,1,1,0.996835,0.974684,1,1,0.993711,1,1,0.955696,0.993671,1,0.981132,0.971519,1,1,0.996835,1,0.987421,0.990506,1,1,0.990506,0.990506,1,1,0.974684,0.930818,1,1,0.943038,1,0.962025,1,1,0.990506,1,0.993671,1,1,0.981013,0.996835,0.993711,1,0.987342,0.981013,1,1,0.987342,0.987421,1,1,0.996835,0.984177,0.996835,0.974684,1,0.993671,0.835443,1]
area_under_roc_curve
0.985936380463339 [0.974843,1,1,1,1,0.987421,0.993671,1,1,1,1,0.993671,1,0.971519,1,1,0.996835,0.936709,0.96519,0.981132,1,0.993711,1,1,1,0.943038,0.974684,0.962025,1,0.993711,1,1,1,0.990506,0.993671,0.974843,0.968553,1,0.987421,1,1,1,1,0.987342,1,1,1,1,0.993671,1,1,0.968354,1,1,0.993711,0.968553,1,1,0.993711,0.876582,1,0.981132,1,0.974684,1,0.993671,1,1,0.792453,0.974843,0.943038,1,0.996835,0.984177,0.987421,0.993671,0.987342,0.936709,1,0.981013,0.952532,0.987342,0.981132,1,0.971519,0.996835,0.996835,1,1,1,0.990506,1,1,0.936709,0.996835,1,0.993711,0.958861,0.984177,1]
area_under_roc_curve
0.987351474802962 [0.996835,1,1,1,1,0.996835,0.987421,0.996835,0.987342,1,1,1,1,0.987342,1,1,1,0.981013,0.962264,0.911392,1,0.987421,0.993671,1,1,0.990506,0.993671,0.987421,1,1,0.987421,1,0.984177,0.971519,1,1,0.993711,0.937107,1,1,0.977848,1,1,1,0.990506,1,0.974684,1,0.974684,1,1,1,1,1,1,0.984177,1,1,0.974843,0.93038,1,0.993711,0.996835,1,0.96519,0.974684,0.984177,0.968354,0.981132,0.952532,0.886076,1,1,0.993671,0.993711,0.996835,1,0.914557,0.993671,1,0.993711,1,0.981013,1,1,1,1,0.984177,0.996835,1,0.996835,1,0.981132,0.981013,0.996835,1,0.981013,0.977848,0.993711,0.96519]
area_under_roc_curve
0.9904969996417481 [0.974684,0.996835,1,1,0.996835,0.984177,1,1,0.990506,1,0.981013,1,1,0.968354,1,1,1,1,0.937107,0.968354,1,1,0.996835,1,0.987421,0.977848,0.939873,1,1,0.993671,1,0.996835,1,0.993671,0.987421,1,1,0.993711,1,0.996835,0.996835,1,1,1,1,1,0.996835,1,0.990506,0.981013,0.993671,1,0.949686,0.924051,1,0.984177,1,0.974684,1,1,0.993711,0.987421,0.993671,1,0.993671,1,0.958861,0.993671,0.993711,0.971519,0.977848,0.998418,0.981132,0.996835,0.993711,0.990506,0.996835,0.971519,1,0.993671,0.955975,0.990506,1,0.993671,1,1,1,1,1,1,0.984177,1,0.981132,0.996835,1,0.993711,0.987342,0.990506,1,0.993671]
area_under_roc_curve
0.9856977947615633 [0.955696,1,1,1,1,0.993671,0.993711,1,1,1,0.990506,0.968354,1,0.861635,1,1,1,0.993671,1,0.987342,0.993671,1,1,1,0.990506,0.977848,0.977848,1,1,0.990506,1,0.974843,1,0.971519,1,1,0.993671,0.911392,0.981013,1,1,1,1,1,0.981132,1,1,0.996835,0.984177,1,1,0.968553,0.996835,0.993711,0.949367,0.971519,1,0.971519,0.993671,0.987421,1,1,1,1,0.990506,1,0.930818,0.943038,0.924051,0.974684,0.984177,1,1,0.993711,0.89557,1,0.993671,0.984177,1,1,1,1,0.987342,0.996835,0.974843,0.981132,1,1,1,1,1,0.987342,0.949686,0.993711,0.889241,0.987421,0.990506,1,0.987421,0.993671]
area_under_roc_curve
0.9906750059708622 [0.939873,1,0.996835,1,1,0.984177,1,0.974684,1,1,0.974684,1,1,0.993711,0.987421,1,1,0.987342,0.993711,0.939873,1,1,0.990506,1,1,0.987342,0.990506,1,1,1,1,1,0.971519,1,1,0.996835,0.990506,0.990506,0.990506,0.984177,1,0.993671,1,0.974843,1,1,0.996835,0.987342,1,0.990506,1,1,0.987342,1,1,0.974684,1,1,0.987342,1,1,1,0.987421,1,0.996835,1,1,1,0.943038,1,0.993671,0.990506,1,1,0.939873,0.968553,1,0.968354,1,1,0.962264,0.984177,1,0.996835,0.949686,1,1,0.993711,0.977848,1,1,0.993671,0.955975,1,1,1,1,0.974843,0.993711,0.993671]
area_under_roc_curve
0.9922316595016321 [1,0.993711,0.987421,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,0.955696,1,1,0.993671,0.977848,0.996835,0.996835,0.987342,1,0.990506,0.899371,0.974843,0.987342,0.993711,0.996835,1,0.962264,1,0.968553,1,1,0.993671,0.990506,1,0.993711,1,1,1,0.987342,0.968553,0.990506,0.990506,0.990506,1,1,0.949686,1,1,1,0.990506,1,1,0.996835,0.987342,0.993711,1,1,1,0.993671,0.996835,1,0.930818,1,0.981013,0.974684,0.993711,1,1,0.993711,1,0.968553,1,0.993711,1,1,0.981013,1,1,1,1,0.993671,1,0.930818,1,1,1,1,0.981013,0.918239,0.987421,0.990506,1,0.993711,0.996835,1]
area_under_roc_curve
0.9902523186848178 [1,1,1,0.990506,1,0.996835,1,1,0.993711,1,0.987421,1,1,1,0.981132,0.987342,1,0.974843,0.962025,0.987342,1,1,1,1,1,0.955975,0.949686,0.968354,0.924528,0.987342,1,1,1,0.993711,1,1,0.984177,0.96519,0.990506,1,1,1,1,0.996835,0.993711,0.993671,1,1,0.993711,1,0.993711,0.993671,0.977848,1,0.996835,0.962025,1,1,0.974684,0.943396,1,0.993671,1,1,0.993671,0.955975,1,0.981013,0.96519,0.987342,0.981132,1,0.993671,1,0.990506,1,1,0.981132,1,1,0.977848,0.974843,0.984177,1,1,0.984177,1,0.943396,1,1,0.993711,0.996835,1,0.924528,1,0.990506,0.971519,0.981132,0.984177,1]
area_under_roc_curve
0.989141489531088 [0.981132,0.996835,1,1,1,0.993711,0.974684,0.987421,0.990506,1,0.993711,1,1,1,0.990506,1,1,1,1,1,1,0.990506,0.943396,0.996835,0.996835,0.855346,0.974843,0.996835,1,1,0.993671,1,1,1,1,1,1,0.968354,0.996835,0.974843,0.993711,0.981013,0.996835,0.981013,0.993671,1,1,0.996835,0.974843,1,1,0.977848,1,0.96519,0.987342,0.968553,0.996835,0.993711,1,0.977848,1,1,1,0.984177,1,1,0.958861,1,0.955696,1,1,0.981132,0.971519,0.981013,0.958861,0.974684,1,0.993711,1,0.993671,0.981013,0.962264,1,0.993711,1,1,1,0.984177,0.981132,1,0.993671,0.996835,1,0.968354,0.962264,0.990506,0.993711,0.996835,0.943038,1]
area_under_roc_curve
0.9840943694769528 [0.874214,1,0.962264,1,1,0.993711,1,0.955975,0.996835,1,1,1,0.996835,0.984177,1,0.987421,1,0.962264,0.987342,1,1,1,1,1,1,0.867925,0.987421,0.990506,0.993671,1,1,0.993671,1,0.993711,0.987342,0.993671,0.974684,0.987342,0.968354,1,0.993711,1,0.987342,0.990506,0.993671,1,1,0.981013,0.993711,0.993711,0.974684,0.996835,0.993671,0.971519,1,0.949686,1,0.849057,0.993671,0.984177,1,1,0.724684,0.981013,1,1,0.993671,0.962264,0.958861,0.968553,0.937107,1,0.955696,1,0.939873,0.990506,1,0.987421,0.987421,0.993671,0.996835,0.987421,1,0.968553,0.996835,0.987342,0.990506,0.987342,1,0.996835,0.996835,0.996835,0.984177,0.996835,1,1,0.930818,1,0.990506,1]
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
0.5137353962740765
kappa
0.6022256422398484
kappa
0.5516792296460002
kappa
0.5578889195910472
kappa
0.5894034505902326
kappa
0.5328098488734562
kappa
0.5704867553590461
kappa
0.6146557169930512
kappa
0.4759273875295975
kappa
0.4823028055084244
kb_relative_information_score
99.40970988757772
kb_relative_information_score
101.68591978942057
kb_relative_information_score
102.05252383273961
kb_relative_information_score
101.69255222370144
kb_relative_information_score
100.7512163673193
kb_relative_information_score
102.87624750490033
kb_relative_information_score
104.39965380830319
kb_relative_information_score
102.52645146360003
kb_relative_information_score
100.98306983311176
kb_relative_information_score
99.63392926048544
mean_absolute_error
0.015305768388454669
mean_absolute_error
0.015015444387213256
mean_absolute_error
0.014946866838861322
mean_absolute_error
0.015280277950832236
mean_absolute_error
0.01507615837624023
mean_absolute_error
0.015078881707729003
mean_absolute_error
0.014878507835077165
mean_absolute_error
0.015122876019632326
mean_absolute_error
0.015294084560182285
mean_absolute_error
0.015220168940804652
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
0.51875
predictive_accuracy
0.60625
predictive_accuracy
0.55625
predictive_accuracy
0.5625
predictive_accuracy
0.59375
predictive_accuracy
0.5375
predictive_accuracy
0.575
predictive_accuracy
0.61875
predictive_accuracy
0.48125
predictive_accuracy
0.4875
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
0.51875 [1,0.5,1,1,0.5,1,0.5,1,0,1,0,0.5,0.5,0,0.5,0,1,0,0,0,1,0,0,1,0,0,0,1,0.5,0,0.5,1,0.5,0,0.5,0,1,1,0,0.5,1,1,1,0,0,1,1,1,1,0.5,1,0.5,1,0,1,0,1,1,0,0,1,1,0.5,0.5,1,1,0,0,0,1,0,1,0,1,0,0,1,0.5,1,0,0,1,0,1,0.5,0.5,1,1,0.5,0,1,1,1,0.5,0.5,0.5,1,0,0,1]
recall
0.60625 [0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,0.5,0,0,1,1,1,1,1,1,0,0,0,1,1,1,1,0.5,0,0.5,0,0,1,0,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0,1,0,1,1,0,0.5,1,1,1,0.5,1,0,0,1,0,0,0,1,1,0.5,0,0,0.5,0.5,1,0,0,0.5,0,1,0.5,0.5,0.5,1,1,1,0.5,1,0.5,0,1,1,0,0.5,0.5,1]
recall
0.55625 [0,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0,1,1,1,0,1,0.5,0,1,1,1,0,1,1,0.5,1,1,1,0,1,0.5,1,0.5,1,1,1,1,0,1,0,1,1,0,0.5,1,1,0.5,1,0,0.5,0.5,0,0,0,0,1,0,0,0,0,1,0,0.5,0.5,0,1,0,0.5,1,1,1,0.5,1,1,0.5,1,0,0,1,1,0,0.5,0,0.5]
recall
0.5625 [0,1,1,1,1,0,1,0.5,0.5,1,0.5,1,1,0,1,0.5,1,1,0,0,1,1,0.5,1,0,0,0,0,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,0,1,1,0,0,1,0.5,1,0.5,0,0.5,1,0,0.5,1,1,1,0,0.5,0,0,0,0.5,0,0.5,0,0,0.5,0,1,0.5,0,0.5,1,1,1,0,1,1,0.5,1,0.5,1,0,0,0.5,0,0,0,0,1]
recall
0.59375 [0,0,1,1,1,1,0,0.5,1,1,0.5,0.5,1,0,1,1,1,0.5,0,0,1,1,0.5,1,0,0,0,0,1,0.5,1,0,1,0.5,1,0.5,1,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,0.5,0,1,0.5,0,1,1,1,1,1,0.5,1,0,0,0,0.5,0,1,1,1,0.5,0,1,0,1,1,1,1,0.5,1,0,0,1,0,1,1,1,0,0,0,0,0,0.5,0,0,1]
recall
0.5375 [0,1,1,1,1,0,1,0,1,0.5,0,1,0,0,1,1,1,0.5,0,0,1,0.5,0.5,1,1,0,0.5,0,0,1,1,1,0.5,0.5,1,0.5,0.5,0.5,0,0,1,0.5,0,1,1,1,1,0,0.5,0.5,1,1,0.5,0,0.5,0,1,1,0,1,1,1,0,1,0.5,0.5,1,0,0,0,0,0,1,1,0.5,0,1,0,1,0,0,0.5,0.5,1,0,1,1,1,0,1,1,0.5,0,0,0.5,1,1,0,0,1]
recall
0.575 [0.5,0,0,0.5,1,0,0.5,1,1,1,1,1,1,0,1,0,1,1,0.5,0,1,0,0,1,0.5,0,0,0,0,1,0.5,0,1,1,1,1,0.5,1,0,1,0.5,0.5,1,0.5,0,0.5,0.5,0.5,1,1,0,1,1,0,0.5,0,0.5,0.5,0,0,1,1,1,1,0.5,1,0,0.5,0,0,0,0.5,1,1,0.5,0,1,0,1,1,0,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,0,0,0.5,1,0,0.5,1]
recall
0.61875 [1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,0,0,0.5,1,1,0,0,1,0.5,0.5,1,1,0,0,0.5,0,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,0,1,1,0.5,1,1,0,0.5,0.5,1,1,0.5,1,1,0,0,1,1,1,1,1,0,1,0,0,0.5,0,0.5,0.5,1,0.5,0,1,0,1,1,0,0,0.5,1,0,0.5,1,0,0,1,0,0.5,0.5,0,1,0.5,0,0,0.5,1]
recall
0.48125 [0,1,1,1,1,0,0.5,0,0,1,0,1,1,0.5,0.5,0,1,1,1,1,1,0,0,1,0.5,0,0,0.5,1,1,0.5,1,1,1,0.5,0.5,0,0,0.5,0,1,0.5,0.5,0,0,1,1,0.5,0,1,0.5,0.5,0.5,0.5,0.5,0,0.5,0,0,0,1,1,0,0.5,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,1,1,0.5,1,1,1,1,0,0,0,0.5,1,0,0,1]
recall
0.4875 [0,1,0,1,1,1,1,0,0.5,1,1,1,1,0,0.5,0,1,0,0,0,0.5,1,1,1,1,0,0,0,1,0,1,1,1,1,0.5,0,0.5,0,0,1,1,1,0.5,0,0.5,0.5,1,0.5,1,1,0.5,1,0,0,1,0,1,0,0,0,1,1,0,0.5,1,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0.5,0.5,0,1,0.5,0.5,1,0,0,0,1,0,0,0.5,1]
relative_absolute_error
0.7730186054775072
relative_absolute_error
0.7583557771319813
relative_absolute_error
0.7548922645889543
relative_absolute_error
0.7717312096379904
relative_absolute_error
0.7614221402141517
relative_absolute_error
0.7615596822085342
relative_absolute_error
0.7514397896503605
relative_absolute_error
0.7637816171531465
relative_absolute_error
0.7724285131405182
relative_absolute_error
0.7686954010507389
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
0.08358096632451549
root_mean_squared_error
0.0817877632979495
root_mean_squared_error
0.08137312385612892
root_mean_squared_error
0.08312384248814765
root_mean_squared_error
0.08233359719803784
root_mean_squared_error
0.0819778623479082
root_mean_squared_error
0.08116432286264476
root_mean_squared_error
0.08184233684287895
root_mean_squared_error
0.0834891353360484
root_mean_squared_error
0.08357595846442877
root_relative_squared_error
0.8400203179204477
root_relative_squared_error
0.8219979493990869
root_relative_squared_error
0.8178306662118103
root_relative_squared_error
0.8354260505023874
root_relative_squared_error
0.8274837865034789
root_relative_squared_error
0.8239085167376203
root_relative_squared_error
0.8157321372686576
root_relative_squared_error
0.8225464341627553
root_relative_squared_error
0.8390973817602272
root_relative_squared_error
0.8399699870328409
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
2505.0165469999683
usercpu_time_millis
2337.3113540001214
usercpu_time_millis
2423.7208449999343
usercpu_time_millis
2395.5721079998966
usercpu_time_millis
2338.8145310000255
usercpu_time_millis
2359.467243999916
usercpu_time_millis
2255.455349000158
usercpu_time_millis
2239.1252470001746
usercpu_time_millis
2360.9007370000654
usercpu_time_millis
2384.7109960001944
usercpu_time_millis_testing
34.3411510000351
usercpu_time_millis_testing
32.907971999975416
usercpu_time_millis_testing
32.804476999899634
usercpu_time_millis_testing
32.897318999857816
usercpu_time_millis_testing
32.90960100002849
usercpu_time_millis_testing
32.788561999950616
usercpu_time_millis_testing
32.56884200004606
usercpu_time_millis_testing
32.6305720000164
usercpu_time_millis_testing
32.85388600011174
usercpu_time_millis_testing
33.21381800014933
usercpu_time_millis_training
2470.6753959999332
usercpu_time_millis_training
2304.403382000146
usercpu_time_millis_training
2390.9163680000347
usercpu_time_millis_training
2362.674789000039
usercpu_time_millis_training
2305.904929999997
usercpu_time_millis_training
2326.678681999965
usercpu_time_millis_training
2222.886507000112
usercpu_time_millis_training
2206.494675000158
usercpu_time_millis_training
2328.0468509999537
usercpu_time_millis_training
2351.497178000045