6006675
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)
4041405
bootstrap
false
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
0.7224259863039381
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
17
6902
min_samples_split
3
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
28314
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"
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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
12936439
description
https://api.openml.org/data/download/12936439/description.xml
-1
12936440
predictions
https://api.openml.org/data/download/12936440/predictions.arff
area_under_roc_curve
0.9854371843434339 [0.973722,0.997199,0.999132,0.997751,0.993529,0.984375,0.993924,0.993924,0.994003,0.996449,0.99562,0.994949,0.992622,0.969697,0.995107,0.989623,0.999092,0.966777,0.969855,0.954033,0.994989,0.99345,0.983389,0.999921,0.987966,0.956597,0.964173,0.986821,0.993884,0.997041,0.998422,0.999369,0.98836,0.977154,0.991438,0.996725,0.970644,0.975497,0.995462,0.998856,0.973643,0.993924,0.997554,0.991004,0.993647,0.979443,0.995739,0.986742,0.960503,0.986861,0.975458,0.985006,0.990294,0.956637,0.986111,0.953046,1,0.998224,0.992227,0.971315,0.996252,0.990372,0.990451,0.995541,0.994003,0.988479,0.967448,0.998935,0.942235,0.95348,0.972341,0.996686,0.986545,0.992582,0.951744,0.986821,0.996962,0.952415,0.997199,0.996804,0.984691,0.989031,0.982165,0.99128,0.987729,0.969066,0.998067,0.980942,0.99416,0.997633,0.997988,0.998382,0.985519,0.978575,0.987966,0.99416,0.991004,0.987453,0.954703,0.976997]
average_cost
0
kappa
0.5467171717171717
kb_relative_information_score
1004.8517179392782
mean_absolute_error
0.01534238883010719
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
0.55125
prior_entropy
6.6438561897747395
recall
0.55125 [0.4375,0.8125,0.9375,0.5625,0.75,0.3125,0.75,0.875,0.5,0.5,0.4375,0.8125,0.625,0.3125,0.8125,0.5625,0.9375,0.375,0.4375,0,0.75,0.5625,0.5,1,0.4375,0.0625,0.375,0.625,0.75,0.875,0.5,1,0.6875,0.4375,0.6875,0.8125,0.25,0.5,0.25,0.9375,0.5,0.5625,0.8125,0.375,0.6875,0.4375,0.875,0.375,0.25,0.75,0.4375,0.4375,0.6875,0.125,0.4375,0.0625,1,0.875,0.5,0.375,0.75,0.5,0.5,0.75,0.8125,0.375,0.3125,1,0.125,0.125,0.0625,0.9375,0.5625,0.8125,0.125,0.75,0.875,0,0.8125,0.6875,0,0.5,0.6875,0.1875,0.625,0.75,0.6875,0.6875,0.375,0.6875,1,1,0.3125,0,0.25,0.6875,0.5625,0.4375,0,0.5]
relative_absolute_error
0.774868122732685
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08282866975408343
root_relative_squared_error
0.8324594529047071
total_cost
0
area_under_roc_curve
0.9854908048722233 [1,1,1,1,0.996835,1,1,1,0.993671,1,0.996835,0.984177,0.987342,0.936709,1,1,1,0.952532,0.946203,0.968553,1,0.987421,1,1,0.993711,0.974684,0.971519,1,0.984177,0.987421,0.990506,1,0.984177,0.971519,1,0.987421,1,0.993711,0.993711,1,1,1,1,1,1,0.993671,1,1,0.993671,0.962025,0.870253,1,0.993711,0.987342,1,0.987421,1,1,0.987421,0.971519,1,1,0.993671,0.990506,1,0.996835,0.993671,1,0.987421,0.937107,0.946203,1,0.987342,0.990506,1,1,1,0.96519,0.996835,0.996835,1,0.984177,1,1,0.981013,0.841772,1,0.993671,0.996835,0.987421,1,1,0.993671,0.987342,1,1,1,0.996835,0.860759,1]
area_under_roc_curve
0.9861005791736324 [0.993711,1,0.993671,1,1,1,0.977848,1,0.993671,1,1,1,0.993671,0.927215,1,1,1,0.946203,0.996835,0.987421,1,0.968553,0.993711,1,1,0.943038,0.968354,0.962025,0.993671,1,1,1,1,0.990506,0.993671,1,0.90566,1,0.993711,1,0.987421,1,1,0.993671,1,0.977848,1,1,0.974684,1,1,0.952532,1,0.984177,0.987421,0.893082,1,1,0.993711,0.920886,1,0.993711,0.996835,0.993671,1,0.993671,0.993671,1,0.773585,0.993711,0.981013,1,0.981013,1,1,1,1,0.943038,1,1,0.981013,0.990506,0.937107,0.981132,0.996835,0.996835,0.993671,0.993671,0.971519,1,0.993671,1,0.996835,0.968354,0.993671,0.996835,0.993711,0.971519,0.987342,1]
area_under_roc_curve
0.9832067012976674 [0.974684,0.993671,1,1,0.974684,0.974684,0.993711,1,0.974684,0.993671,0.996835,1,1,0.984177,0.949367,0.996835,1,0.990506,0.899371,0.920886,1,0.981132,0.905063,1,0.930818,0.993671,1,0.993711,1,1,0.993711,1,1,0.955696,0.974843,1,1,0.924528,1,1,0.977848,1,1,1,0.993671,0.981132,0.993671,1,0.816456,0.993671,0.996835,1,0.993711,0.971519,1,0.993671,1,0.993671,0.993711,0.949367,1,0.974843,0.996835,1,0.996835,0.971519,0.984177,0.996835,0.974843,0.905063,0.990506,0.993671,0.968553,0.990506,1,0.996835,1,0.93038,0.993671,0.996835,0.987421,1,1,0.996835,1,1,1,0.996835,0.996835,1,0.996835,1,0.974843,0.955696,0.993671,1,0.977848,0.981013,0.993711,0.990506]
area_under_roc_curve
0.9853057081442562 [1,0.996835,1,1,0.987342,0.971519,1,0.993671,0.996835,0.996835,0.993671,0.996835,1,0.996835,1,1,1,0.96519,0.842767,0.990506,1,1,1,1,0.949686,0.971519,0.905063,1,0.993671,0.993671,1,1,1,0.996835,1,0.981132,1,1,1,1,0.987342,1,1,0.987421,1,1,0.993671,1,0.984177,1,0.990506,1,0.962264,0.911392,1,0.990506,1,1,1,0.987342,1,0.987421,0.990506,1,0.984177,0.990506,0.952532,1,0.993711,0.933544,0.955696,0.987342,0.987421,1,0.974843,0.949367,0.996835,0.914557,1,0.993671,0.937107,0.981013,1,0.987342,1,1,1,1,1,1,1,1,0.924528,0.981013,1,0.974843,0.984177,0.993671,0.968553,0.920886]
area_under_roc_curve
0.9825421940928265 [0.990506,1,1,1,0.993711,0.977848,0.955975,1,0.993711,0.996835,1,0.984177,1,0.855346,1,0.981013,1,0.993671,0.993711,0.993671,0.987342,0.990506,1,1,0.993671,0.939873,0.977848,1,1,0.993671,1,1,0.971519,0.933544,1,1,0.971519,0.952532,0.981013,0.996835,1,0.990506,0.981132,1,1,0.974843,0.996835,0.981013,0.996835,0.993671,0.981132,0.949686,0.990506,0.987421,0.955696,0.924051,1,0.987342,0.993671,0.968553,0.993671,1,0.993711,1,0.993671,1,0.81761,1,0.882911,0.927215,0.996835,1,0.993711,0.968553,0.889241,1,0.987342,0.939873,1,0.993711,1,1,0.987342,0.993671,0.981132,1,1,1,1,1,1,0.993671,0.981132,0.987421,0.968354,1,0.990506,0.993711,0.987421,0.974684]
area_under_roc_curve
0.9836756627657035 [0.908228,1,1,1,1,0.984177,0.987421,1,1,0.993671,1,1,1,0.993711,1,0.996835,1,1,1,0.901899,0.993671,0.987342,0.981013,1,0.990506,0.977848,0.974684,0.993711,1,1,1,1,0.952532,0.987342,0.962264,1,0.914557,0.968354,0.993671,1,1,0.981013,1,1,1,1,0.993671,0.984177,0.939873,0.955696,0.993711,0.974843,0.996835,1,0.981013,0.936709,1,1,1,1,1,0.981013,0.981132,1,1,0.996835,1,1,0.974684,1,0.943038,0.990506,1,1,0.844937,1,0.996835,0.958861,1,0.993711,0.987421,0.981013,1,0.984177,0.968553,1,1,0.955975,0.987342,1,1,0.996835,0.943396,1,1,1,0.993671,0.974843,0.968553,0.949367]
area_under_roc_curve
0.9881565460552502 [0.990506,1,1,0.990506,1,1,1,1,0.993711,1,1,1,0.971519,0.981132,0.987421,0.974684,0.987421,0.937107,0.984177,0.968354,0.981013,0.996835,1,1,0.984177,0.886792,0.993711,0.977848,1,1,1,0.993711,1,0.993711,1,0.996835,0.993671,0.993671,1,0.987421,0.977848,0.993671,1,0.990506,1,1,0.984177,0.974684,1,1,0.993711,0.987342,0.993671,0.962264,0.993671,0.96519,1,0.996835,0.993671,0.974843,0.987342,0.993671,1,0.981013,0.996835,0.987421,0.955975,1,0.96519,0.952532,0.993711,1,0.996835,0.993711,0.993671,1,1,0.955975,1,1,0.987342,1,0.958861,0.993671,1,1,0.996835,0.880503,1,1,1,1,0.974684,1,0.974843,0.987342,1,0.987421,0.93038,1]
area_under_roc_curve
0.988311042114481 [1,1,1,0.996835,1,0.984177,0.996835,1,0.993711,0.996835,0.987421,1,0.990506,1,1,0.993671,1,0.943396,0.990506,0.917722,0.990506,0.993671,1,1,0.996835,0.974843,0.867925,0.971519,1,0.996835,1,1,1,0.987421,0.990506,1,0.987342,0.968354,1,1,1,1,1,0.984177,1,0.993671,1,1,1,1,0.981132,0.987342,0.977848,1,0.996835,0.949367,1,1,0.987342,1,0.993671,0.984177,0.993711,0.996835,1,0.968553,0.962264,1,0.936709,0.971519,0.993711,1,1,0.924528,0.981013,1,1,0.974843,1,0.993711,0.987342,0.993711,0.958861,1,0.968354,0.971519,1,0.993711,1,1,1,1,0.993671,0.974843,0.987421,1,0.990506,1,0.962025,0.981013]
area_under_roc_curve
0.9903655162805505 [0.962264,1,1,1,1,1,0.981013,1,0.996835,1,0.987421,1,1,0.996835,1,0.987421,1,1,1,1,1,1,0.993711,0.996835,0.996835,1,0.981132,1,0.987342,1,1,1,0.993711,0.981132,0.996835,1,0.993671,0.955696,0.996835,1,0.830189,1,0.996835,0.984177,0.981013,0.962025,0.993711,0.987342,0.968553,1,1,0.996835,0.996835,0.882911,0.987342,0.949686,1,1,0.993671,0.971519,0.990506,1,1,1,1,1,0.993671,1,0.977848,0.974843,1,1,0.984177,1,0.981013,0.962025,1,0.993711,1,0.993671,0.993671,1,0.993711,0.993711,0.993671,1,1,0.984177,1,1,1,0.996835,1,0.987342,0.987421,0.977848,0.987421,1,0.993671,1]
area_under_roc_curve
0.9917567271714034 [0.937107,0.993671,1,1,1,0.993711,1,0.930818,0.990506,1,1,1,0.993671,0.987342,1,1,1,0.993711,0.990506,0.993711,1,1,1,1,1,0.90566,1,0.996835,0.990506,1,1,1,1,0.974843,0.990506,0.996835,0.981013,1,1,1,0.987421,1,0.996835,0.987342,1,0.949367,0.993711,0.981013,1,0.987421,0.996835,0.987342,1,0.981013,0.993671,0.955975,1,1,0.977848,0.996835,1,0.993671,0.974684,1,0.987421,0.993711,0.990506,1,1,0.962264,0.918239,1,0.977848,1,0.974684,0.996835,1,1,0.981132,1,1,0.962264,1,0.949686,0.993671,1,1,0.996835,1,1,1,1,1,0.996835,0.993711,1,1,1,0.990506,0.987421]
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
0.5705545687783697
kappa
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kappa
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kappa
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kappa
0.4757618822043265
kappa
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kappa
0.51356260117661
kappa
0.5705206647455887
kappa
0.5453827940015786
kappa
0.6084004421285332
kb_relative_information_score
101.34853613177988
kb_relative_information_score
99.5081474452757
kb_relative_information_score
98.13201792598505
kb_relative_information_score
98.84015439603294
kb_relative_information_score
99.01083733154422
kb_relative_information_score
99.62389884375328
kb_relative_information_score
101.01149488132569
kb_relative_information_score
100.90423428551848
kb_relative_information_score
102.9073141766691
kb_relative_information_score
103.56508252139561
mean_absolute_error
0.01526437117145161
mean_absolute_error
0.015347856014318683
mean_absolute_error
0.01537939277405953
mean_absolute_error
0.015457731783424708
mean_absolute_error
0.015351293352426323
mean_absolute_error
0.015336687318775822
mean_absolute_error
0.015381091192271443
mean_absolute_error
0.015376498326066385
mean_absolute_error
0.01531801494383223
mean_absolute_error
0.015210951424445163
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
0.575
predictive_accuracy
0.56875
predictive_accuracy
0.53125
predictive_accuracy
0.575
predictive_accuracy
0.48125
predictive_accuracy
0.525
predictive_accuracy
0.51875
predictive_accuracy
0.575
predictive_accuracy
0.55
predictive_accuracy
0.6125
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
0.575 [1,1,1,1,0.5,1,1,1,1,0,0.5,0.5,0.5,0,1,1,1,0,0,0,1,0,1,1,0,0,0,1,0.5,1,0.5,1,0.5,0,1,0,0,1,1,1,1,0,1,0.5,1,0.5,1,1,0,0.5,0.5,1,1,0,1,0,1,1,0,0.5,1,1,0,0.5,1,0.5,0.5,1,0,0,0,1,0.5,1,1,1,1,0,1,1,0,0.5,1,0,0,0.5,1,0.5,0,0,1,1,1,0,0,0.5,1,0.5,0,1]
recall
0.56875 [1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,0.5,0.5,1,1,1,0,0.5,0,1,0,0,1,1,0,0.5,0,0.5,1,0.5,1,0.5,0.5,1,1,0,1,0,1,1,1,1,0,0.5,0,1,0,0,1,0,0.5,1,0,1,0,1,1,1,0,1,1,1,1,1,0,0.5,1,0,0,0,1,0.5,1,0,1,0.5,0,1,0.5,0,0.5,0,0,1,0.5,0.5,1,0.5,1,1,1,0,0,0.5,0.5,1,0,0,1]
recall
0.53125 [0,0.5,1,1,0.5,0,1,1,0,0.5,0,1,1,0.5,0,0.5,1,0.5,0,0,1,0,0,1,0,0.5,0.5,1,1,1,0,1,1,0,0,1,1,0,1,1,0,1,1,1,0.5,0,0.5,0,0.5,0.5,1,1,1,0.5,1,0,1,1,1,0,1,0,0,1,0.5,0.5,0,1,0,0,0,1,0,0.5,0,0.5,1,0,0.5,1,0,0.5,1,0.5,1,0,1,1,1,1,1,1,0,0,0.5,1,0,0.5,0,0.5]
recall
0.575 [1,1,1,1,0.5,0,1,1,0.5,0.5,0,0.5,0,0.5,1,1,1,0.5,0,0,0,1,0.5,1,0,0,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,0,1,0.5,0,1,1,1,1,1,1,0,1,0,1,0,0,1,0.5,1,1,1,0.5,1,0,1,1,0.5,0,0,1,0,0.5,0,0.5,0,1,0,0.5,1,0,1,1,0,0.5,1,0,1,1,1,1,0,1,1,1,0,0,0,0,0.5,0.5,0,0]
recall
0.48125 [0.5,1,1,1,0,0,0,1,0,1,0,0.5,1,0,1,0.5,1,1,1,0,0.5,0,1,1,0.5,0,0.5,1,1,0.5,0,1,0,0,0,1,0,0,0,1,1,0.5,0,0,1,0,0.5,0,0.5,0.5,0,0,0,0,0,0,1,0.5,1,0,0.5,1,1,1,0.5,0,0,1,0,0,0,1,0,0,0,1,0.5,0,0.5,1,0,1,0.5,0,0,1,1,1,1,1,1,1,0,0,0.5,1,0.5,1,0,0.5]
recall
0.525 [0,1,1,0,1,0.5,1,0.5,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0,1,0.5,0,1,0.5,0,0.5,1,1,1,0,1,0.5,1,0,0.5,0,0.5,0,1,1,0.5,1,1,1,1,1,0,0,0.5,1,0,1,0,0,0,1,1,0,1,0.5,0.5,0,1,1,0.5,1,1,0,0.5,0,1,1,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,0.5,1,1,0,0,0,1,0.5,0,0,0.5]
recall
0.51875 [0.5,1,1,0,1,0,1,1,1,0,1,1,0.5,0,0,0,0,0,0.5,0,1,1,0.5,1,0.5,0,0,0.5,0,1,0.5,1,1,0,1,1,0.5,1,0.5,0,0,0,1,0.5,0,0.5,1,0.5,0,1,0,0.5,0.5,0,0.5,0,1,0.5,0.5,0,0,0,1,0,1,1,0,1,0,0,1,1,1,1,0.5,1,1,0,1,1,0,1,0.5,0.5,1,1,0.5,0,0,0.5,1,1,0,0,0,0.5,1,0,0,0.5]
recall
0.575 [0.5,1,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0,1,0.5,1,0,0,0,0.5,1,0.5,1,0.5,0,0,0.5,1,1,0,1,1,1,0.5,1,0,0,0.5,1,0.5,1,1,0,1,0.5,1,1,1,1,0,0,0.5,1,0.5,0,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0,0,1,1,0,0,1,1,0,1,1,0,0,0,0.5,0.5,0.5,1,0,1,0.5,1,1,0,0,0,1,0.5,0,0,0.5]
recall
0.55 [0,1,0,0.5,1,1,0,1,0,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0,0,1,0,0.5,0.5,0.5,0,0.5,1,0,0,1,1,0,1,0,0,0,1,1,0,0,1,1,0.5,0.5,1,1,0,1,0.5,0,0,1,0.5,1,0,0.5,1,0,1,0.5,0,1,1,0,0.5,1,0.5,0.5,0,1,1,1,0.5,0,0,0.5,0,0.5,0,1]
recall
0.6125 [0,0,1,0.5,1,1,1,0,0.5,0,1,1,0.5,0.5,1,0,1,0,0.5,0,0.5,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,0,1,0,1,0,1,0.5,0,1,0.5,1,0.5,1,1,0.5,0.5,1,0,0.5,0,1,1,0,1,1,0,0,1,1,1,0.5,1,0.5,0,0,1,0.5,1,0,1,1,0,0,0,0,0,1,0,1,1,0.5,1,0,0.5,1,1,1,0,1,1,1,1,0,0]
relative_absolute_error
0.7709278369419992
relative_absolute_error
0.7751442431474069
relative_absolute_error
0.7767370087908841
relative_absolute_error
0.780693524415388
relative_absolute_error
0.7753178460821363
relative_absolute_error
0.7745801676149392
relative_absolute_error
0.7768227874884555
relative_absolute_error
0.7765908245488059
relative_absolute_error
0.7736371183753639
relative_absolute_error
0.7682298699214717
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
0.08211438617183212
root_mean_squared_error
0.0827312292630798
root_mean_squared_error
0.08348324887637198
root_mean_squared_error
0.08316772590561242
root_mean_squared_error
0.08352898623426402
root_mean_squared_error
0.08313431198726319
root_mean_squared_error
0.08321403185788318
root_mean_squared_error
0.08259191297141422
root_mean_squared_error
0.08224304020574487
root_mean_squared_error
0.08206101952670429
root_relative_squared_error
0.8252806327948936
root_relative_squared_error
0.8314801391227467
root_relative_squared_error
0.8390382206144993
root_relative_squared_error
0.8358670954425362
root_relative_squared_error
0.8394978983570145
root_relative_squared_error
0.8355312729275667
root_relative_squared_error
0.8363324877735734
root_relative_squared_error
0.8300799577086907
root_relative_squared_error
0.826573654486573
root_relative_squared_error
0.8247442778306237
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
6875.602867001362
usercpu_time_millis
6732.58924700167
usercpu_time_millis
6860.805284999515
usercpu_time_millis
6788.377111000955
usercpu_time_millis
6686.533212998256
usercpu_time_millis
6952.57368700004
usercpu_time_millis
6782.280658999298
usercpu_time_millis
6682.240361000368
usercpu_time_millis
6710.977096001443
usercpu_time_millis
6726.062182999158
usercpu_time_millis_testing
37.032025000371505
usercpu_time_millis_testing
33.74883300057263
usercpu_time_millis_testing
34.43046299980779
usercpu_time_millis_testing
33.795024999562884
usercpu_time_millis_testing
33.86204599883058
usercpu_time_millis_testing
34.27410500080441
usercpu_time_millis_testing
33.78360599890584
usercpu_time_millis_testing
33.808805001172004
usercpu_time_millis_testing
34.02049200121837
usercpu_time_millis_testing
36.90024899879063
usercpu_time_millis_training
6838.5708420009905
usercpu_time_millis_training
6698.840414001097
usercpu_time_millis_training
6826.374821999707
usercpu_time_millis_training
6754.582086001392
usercpu_time_millis_training
6652.671166999426
usercpu_time_millis_training
6918.299581999236
usercpu_time_millis_training
6748.497053000392
usercpu_time_millis_training
6648.431555999196
usercpu_time_millis_training
6676.956604000225
usercpu_time_millis_training
6689.161934000367