4905831
1
Jan van Rijn
9954
Supervised Classification
6970
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)
2941228
class_weight
null
6941
criterion
"gini"
6941
max_depth
4
6941
max_features
null
6941
max_leaf_nodes
null
6941
min_impurity_split
1e-07
6941
min_samples_leaf
1
6941
min_samples_split
2
6941
min_weight_fraction_leaf
0.0
6941
presort
false
6941
random_state
35430
6941
splitter
"best"
6941
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
algorithm
"SAMME.R"
6971
learning_rate
0.31702212960522697
6971
n_estimators
76
6971
random_state
60763
6971
openml-pimp
openml-python
Sklearn_0.18.1.
study_71
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
10745763
description
https://api.openml.org/data/download/10745763/description.xml
-1
10745764
predictions
https://api.openml.org/data/download/10745764/predictions.arff
area_under_roc_curve
0.9827422664141414 [0.967251,0.993016,0.998501,0.999842,0.99053,0.984099,0.996567,0.992069,0.990728,0.999961,0.994831,0.99783,0.982008,0.955019,0.999605,0.978772,0.999408,0.946851,0.971117,0.936514,0.986979,0.990096,0.982757,0.999408,0.988873,0.946674,0.951547,0.983902,0.991359,0.999448,0.997356,0.997593,0.980548,0.981258,0.985361,0.995147,0.965041,0.945549,0.995975,0.999566,0.99053,0.987413,0.999448,0.989938,0.993805,0.99491,0.997396,0.989149,0.984257,0.985953,0.971315,0.983862,0.982323,0.958452,0.988834,0.941465,1,0.998501,0.984099,0.964015,0.994949,0.989504,0.986072,0.989899,0.995857,0.973801,0.948587,0.999645,0.982876,0.970683,0.96441,0.996725,0.973051,0.990885,0.94113,0.988163,0.997909,0.932884,0.998737,0.999605,0.98256,0.993292,0.983744,0.990175,0.977865,0.994673,1,0.990412,0.9927,0.992503,0.964173,0.99925,0.986348,0.973564,0.986466,0.991911,0.969342,0.985835,0.899897,0.979522]
average_cost
0
f_measure
0.5162996284510292 [0.233333,0.48,0.814815,0.857143,0.814815,0.521739,0.692308,0.689655,0.555556,0.857143,0.5,0.608696,0.411765,0.388889,0.896552,0.415094,0.222222,0.173913,0.095238,0.133333,0.222222,0.666667,0.275862,0.857143,0.5,0.157895,0.090909,0.606061,0.615385,0.814815,0.666667,0.769231,0.5,0.142857,0.4375,0.521739,0.16,0.307692,0.785714,0.4,0.487805,0.5625,0.608696,0.482759,0.62069,0.5,0.608696,0.416667,0.580645,0.538462,0.421053,0.47619,0.594595,0.238095,0.533333,0.111111,0.933333,0.72,0.390244,0.4,0.545455,0.705882,0.333333,0.608696,0.454545,0.27027,0.185185,0.814815,0.390244,0.25974,0.472727,0.545455,0.190476,0.827586,0.484848,0.580645,0.814815,0.216216,0.814815,0.857143,0.5,0.758621,0.5,0.424242,0.424242,0.8125,0.896552,0.580645,0.8125,0.6,0.521739,0.740741,0.5,0.55814,0.52381,0.631579,0.363636,0.4375,0.066667,0.518519]
kappa
0.4880050505050505
kb_relative_information_score
930.6339300173772
mean_absolute_error
0.01574489940503141
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
0.6224957939631001 [0.159091,0.666667,1,1,1,0.857143,0.9,0.769231,0.5,1,0.583333,1,0.388889,0.35,1,0.297297,1,0.285714,0.2,0.142857,1,0.714286,0.307692,1,0.75,0.136364,0.071429,0.588235,0.8,1,1,1,0.5,0.166667,0.4375,0.857143,0.222222,0.4,0.916667,1,0.4,0.5625,1,0.538462,0.692308,0.35,1,0.625,0.6,0.7,0.363636,1,0.52381,0.192308,0.571429,0.1,1,1,0.32,0.310345,1,0.666667,0.357143,1,0.833333,0.238095,0.131579,1,0.32,0.163934,0.333333,1,0.4,0.923077,0.470588,0.6,1,0.137931,1,1,0.5,0.846154,0.583333,0.411765,0.411765,0.8125,1,0.6,0.8125,0.642857,0.857143,0.909091,0.583333,0.444444,0.423077,0.545455,0.352941,0.4375,0.045455,0.636364]
predictive_accuracy
0.493125
prior_entropy
6.6438561897747395
recall
0.493125 [0.4375,0.375,0.6875,0.75,0.6875,0.375,0.5625,0.625,0.625,0.75,0.4375,0.4375,0.4375,0.4375,0.8125,0.6875,0.125,0.125,0.0625,0.125,0.125,0.625,0.25,0.75,0.375,0.1875,0.125,0.625,0.5,0.6875,0.5,0.625,0.5,0.125,0.4375,0.375,0.125,0.25,0.6875,0.25,0.625,0.5625,0.4375,0.4375,0.5625,0.875,0.4375,0.3125,0.5625,0.4375,0.5,0.3125,0.6875,0.3125,0.5,0.125,0.875,0.5625,0.5,0.5625,0.375,0.75,0.3125,0.4375,0.3125,0.3125,0.3125,0.6875,0.5,0.625,0.8125,0.375,0.125,0.75,0.5,0.5625,0.6875,0.5,0.6875,0.75,0.5,0.6875,0.4375,0.4375,0.4375,0.8125,0.8125,0.5625,0.8125,0.5625,0.375,0.625,0.4375,0.75,0.6875,0.75,0.375,0.4375,0.125,0.4375]
relative_absolute_error
0.7951969396480496
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08477810868878188
root_relative_squared_error
0.852052051383813
total_cost
0
area_under_roc_curve
0.9849044164477349 [0.968553,0.974684,1,1,0.996835,1,1,1,1,1,0.993671,1,0.984177,0.939873,1,0.974843,1,0.939873,0.968354,0.949686,0.974843,0.993711,0.987421,1,0.993711,0.958861,0.946203,1,1,1,1,1,0.974684,0.936709,0.993671,0.993711,0.993711,0.987421,1,1,0.993711,0.987421,1,1,1,0.990506,0.993711,0.968553,0.996835,0.955696,0.958861,1,0.987421,0.977848,1,0.949686,1,1,0.993711,0.987342,1,0.993711,0.981013,0.987342,1,0.987342,0.958861,1,1,0.943396,0.936709,0.993711,0.993671,1,1,0.996835,1,0.924051,1,1,0.993671,1,0.993711,1,0.968354,0.996835,1,0.990506,1,1,1,1,1,0.946203,0.993671,0.990506,1,0.993671,0.870253,1]
area_under_roc_curve
0.9842744904864261 [0.993711,1,1,1,1,1,0.996835,1,0.993671,1,1,1,0.990506,0.857595,1,1,1,0.933544,0.981013,0.90566,0.987421,0.962264,0.981132,1,1,0.946203,0.96519,0.958861,0.993671,1,1,1,1,0.987342,0.987342,0.974843,1,0.90566,0.987421,1,0.993711,0.993711,1,0.993671,1,0.974684,1,1,0.996835,1,0.993671,0.958861,1,0.981013,1,0.962264,1,1,0.987421,0.920886,1,1,0.996835,0.993671,1,1,0.939873,1,0.955975,1,0.996835,1,0.993671,1,0.993711,0.993671,0.996835,0.990506,1,1,0.977848,0.996835,1,0.987421,0.990506,1,1,0.990506,1,0.987421,0.946203,1,0.996835,0.946203,1,1,0.987421,0.981013,0.832278,0.993711]
area_under_roc_curve
0.988429215428708 [0.974684,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.981013,1,0.977848,0.974843,0.955696,1,0.993711,0.981013,1,0.968553,0.985759,0.981013,0.974843,1,1,1,1,0.977848,0.958861,0.943396,1,0.930818,0.993711,0.993711,1,0.996835,0.943396,1,1,0.993671,1,1,0.987421,0.946203,1,0.996835,1,1,0.990506,0.987421,0.973101,1,0.996835,0.943396,0.96519,1,1,0.990506,0.949686,0.996835,0.949367,0.971519,1,0.993711,0.984177,0.984177,0.996835,1,1,1,0.990506,1,0.955696,0.996835,1,1,0.996835,0.996835,0.990506,1,1,1,0.987342,0.996835,0.993711,0.996835,1,1,0.993671,0.968354,0.987421,0.968354,0.987342,0.949686,0.993671]
area_under_roc_curve
0.9844021176657909 [0.990506,0.993671,1,1,0.990506,0.96519,1,1,0.996835,1,1,0.990506,0.949686,0.996835,1,0.993671,1,0.993671,0.949686,0.911392,0.993711,0.962264,0.993671,1,0.974843,0.955696,0.886076,1,0.993671,1,1,1,0.996835,0.987342,0.981132,1,0.981132,0.836478,1,1,0.993671,1,1,0.993711,1,0.993711,1,0.987421,1,1,0.96519,1,0.987421,0.943038,1,0.943038,1,1,0.993711,0.962025,1,1,0.993671,1,0.996835,0.987342,0.987342,1,1,0.990506,0.892405,1,0.981132,1,0.993711,0.987342,1,0.943038,1,1,0.974843,1,0.981013,0.996835,0.955975,1,1,0.984177,1,1,0.971519,1,0.968553,1,1,0.962264,0.96519,0.981013,0.893082,0.993671]
area_under_roc_curve
0.9878338707109302 [1,1,1,1,1,0.993671,1,0.996835,1,1,0.993671,1,0.987421,0.899371,1,0.993671,1,0.962025,0.968553,0.96519,0.984177,0.996835,0.993671,1,0.990506,0.981013,0.946203,1,1,1,1,1,0.984177,0.974684,0.981132,1,0.958861,0.901899,1,1,0.993671,1,1,0.993711,1,0.993711,1,1,1,1,1,0.955975,0.996835,1,0.984177,0.977848,1,0.996835,0.990506,1,0.993671,1,0.968553,0.993711,0.981013,0.990506,1,1,1,0.943038,0.993671,1,1,1,0.857595,0.993711,1,0.993671,1,1,0.993711,1,1,0.981013,0.962264,1,1,0.987421,1,0.993671,0.987421,1,1,0.993711,0.96519,1,0.996835,1,0.899371,1]
area_under_roc_curve
0.9834823561022208 [0.882911,1,0.984177,1,1,0.958861,1,0.993671,0.993711,1,0.996835,1,0.987421,0.974843,1,0.990506,1,0.943038,1,0.908228,1,0.981013,0.987342,1,1,0.93038,0.939873,1,1,1,1,1,0.993671,0.990506,0.981132,0.993671,0.962025,0.981013,0.996835,1,0.96519,0.996835,1,0.962264,1,1,0.993671,0.990506,0.981013,0.987342,0.993711,1,0.990506,0.981132,0.996835,0.936709,1,1,1,0.974843,0.993671,1,0.987421,1,1,0.962025,0.993711,1,0.987342,0.987342,0.993671,0.993671,1,1,0.984177,0.987421,1,0.860759,1,1,0.974843,0.984177,0.974684,0.981013,0.987421,1,1,1,1,0.987342,0.974843,1,0.981132,1,0.993671,1,0.958861,0.987421,0.842767,1]
area_under_roc_curve
0.986106052463976 [0.974684,1,1,1,1,1,0.996835,1,0.987421,1,1,1,0.996835,0.987421,1,0.943038,1,0.993711,0.987342,0.914557,0.996835,1,0.958861,1,1,0.842767,0.962264,0.990506,1,1,0.996835,0.987421,0.993711,0.993711,1,1,0.971519,0.924051,1,0.993711,0.993671,0.984177,1,1,0.968553,1,0.996835,0.984177,0.987421,1,0.861635,0.996835,0.987342,0.987421,0.974684,0.927215,1,1,0.990506,1,0.996835,0.990506,0.993711,0.993671,1,0.949686,0.949686,1,0.96519,1,1,1,0.996835,0.981132,0.993671,1,1,0.943396,1,1,0.981013,1,1,1,0.990506,0.996835,1,0.993711,0.955975,0.990506,1,1,0.977848,0.943396,1,0.993671,0.981013,0.981132,0.936709,0.996835]
area_under_roc_curve
0.9859438440410795 [0.974684,1,1,1,1,1,0.996835,0.996835,0.993711,1,0.987421,1,1,1,1,0.974684,1,0.924528,0.974684,0.990506,0.987342,1,0.984177,1,0.971519,0.949686,0.993711,0.974684,0.993711,1,1,1,1,0.981132,0.987342,1,0.949367,0.955696,0.996835,1,1,0.996835,1,1,0.993711,0.996835,1,0.996835,0.987421,0.993711,0.90566,0.990506,0.958861,0.962264,1,0.920886,1,1,0.981013,0.91195,1,0.977848,0.993711,1,1,0.974843,0.874214,1,0.987342,0.971519,1,1,0.993671,1,0.974684,1,1,0.949686,1,1,0.939873,1,0.968354,0.996835,0.977848,0.990506,1,1,1,0.993671,0.974843,1,0.996835,1,1,1,0.987342,0.993711,0.939873,0.952532]
area_under_roc_curve
0.9847626084706631 [0.974843,0.984177,1,1,0.96519,0.949686,0.996835,1,0.987342,1,0.968553,1,0.993671,0.993671,1,0.981132,1,0.90566,0.958861,0.886792,0.993671,1,0.955975,1,1,0.91195,0.981132,1,1,1,1,1,1,1,0.993671,1,0.984177,0.977848,1,1,1,1,0.996835,0.977848,0.987342,1,1,0.990506,0.987421,0.993711,0.993671,0.993671,0.996835,0.924051,0.96519,0.930818,1,0.993711,1,0.96519,1,1,0.993671,0.993671,1,1,0.892405,1,0.984177,0.899371,1,0.974843,0.89557,1,1,0.990506,1,0.987421,1,1,0.984177,0.974843,1,0.974843,0.990506,1,1,1,1,1,0.949367,1,0.977848,0.977848,0.993711,0.993671,0.981132,1,0.936709,0.993711]
area_under_roc_curve
0.9885108172120048 [1,1,1,1,1,1,1,1,0.987342,1,1,1,0.974684,0.993671,1,0.949686,1,0.91195,0.990506,0.955975,0.968354,0.987342,1,1,0.990506,0.993711,0.943396,0.987342,0.993671,1,1,1,1,1,0.996835,0.996835,0.971519,0.981013,1,1,1,1,1,1,0.993671,1,1,0.993671,0.993711,0.993711,1,1,1,0.939873,0.996835,0.974843,1,1,0.968354,0.981013,0.993671,0.993671,0.971519,0.993671,0.987421,0.987421,0.958861,1,0.993671,0.962264,1,1,0.971519,0.993671,0.952532,0.984177,1,0.981132,1,1,0.996835,0.974843,0.974843,0.981132,0.993671,1,1,0.993671,1,1,0.946203,0.996835,0.984177,0.971519,1,0.993671,0.880503,0.993671,0.987342,1]
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
0.4947700809157292
kappa
0.5074591522614255
kappa
0.5008687411151477
kappa
0.47569979075368163
kappa
0.4947900221029365
kappa
0.48211889160811555
kappa
0.4884950862375183
kappa
0.5012626262626262
kappa
0.457284846572533
kappa
0.4759067050791271
kb_relative_information_score
94.74603407250119
kb_relative_information_score
94.16007862480836
kb_relative_information_score
92.44043200415622
kb_relative_information_score
90.37776993458885
kb_relative_information_score
98.59478610770441
kb_relative_information_score
91.65775580327907
kb_relative_information_score
91.45692519960846
kb_relative_information_score
91.66605023742837
kb_relative_information_score
93.50251576735285
kb_relative_information_score
92.03158226594972
mean_absolute_error
0.015511259505929062
mean_absolute_error
0.015403934267935807
mean_absolute_error
0.01586508890224021
mean_absolute_error
0.016223722287000534
mean_absolute_error
0.015342704953842027
mean_absolute_error
0.015866180651320325
mean_absolute_error
0.016070598937701037
mean_absolute_error
0.015850664948693794
mean_absolute_error
0.015460742041679423
mean_absolute_error
0.015854097553971668
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
0.5
predictive_accuracy
0.5125
predictive_accuracy
0.50625
predictive_accuracy
0.48125
predictive_accuracy
0.5
predictive_accuracy
0.4875
predictive_accuracy
0.49375
predictive_accuracy
0.50625
predictive_accuracy
0.4625
predictive_accuracy
0.48125
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
0.5 [0,0.5,1,1,0.5,1,0.5,1,1,1,0.5,0.5,0.5,0,0.5,1,0,0.5,0,0,0,1,0,0,0,0.5,0,1,0.5,0,0.5,0.5,0.5,0,0,0,0,1,1,0.5,0,0,0,0.5,1,1,0,0,1,0,0.5,0,1,0,0,0,1,1,1,1,1,1,0.5,0,0,0,0.5,1,1,1,0.5,0,0,1,1,1,0.5,0,1,0.5,0.5,1,0,1,0.5,1,0.5,0.5,0,0,0.5,1,0.5,0.5,0.5,1,1,1,0,1]
recall
0.5125 [1,1,0.5,0,1,1,0.5,1,0.5,1,0,0.5,0.5,0.5,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0.5,0.5,0,0,0,0,0,1,1,0,0.5,0,0.5,0.5,1,1,0,0.5,0.5,0,1,0,1,0,1,1,1,0,1,1,0,0.5,1,1,0,1,0,1,1,1,0,1,1,1,0.5,1,1,1,0.5,0.5,0,0,1,1,1,0.5,1,0,0,1,0.5,0.5,0.5,1,1,0.5,0,0]
recall
0.50625 [0.5,0,1,0,1,1,0,1,1,0.5,0.5,0.5,1,1,0.5,0.5,0,0,0,0,0,0,0,1,0,0.5,0,0,1,1,0,1,0.5,0,0,1,0,0,1,0,0.5,0,1,1,1,1,0,0,0.5,0.5,1,1,1,1,0,0,1,0.5,0,0.5,0,1,0,0,0,0,0,1,0,0.5,1,0.5,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,0,1,1,1,0.5,1,0,1,0,0,1,0.5,0,0,0.5,0,1]
recall
0.48125 [0,0,0.5,1,0.5,0,1,1,0.5,0.5,0.5,0,0,1,1,0.5,0,0,0,0,0,0,1,1,0,0,0,1,0.5,1,1,0.5,0.5,0,1,1,0,0,1,0,0.5,1,0,1,1,1,0.5,0,0.5,1,0.5,0,1,0.5,1,0,1,0.5,0,0,0,1,0.5,0,0,0.5,1,0.5,1,1,0,0,0,1,1,0,1,0,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,0,1,1,0,0,0.5,0,0.5]
recall
0.5 [0.5,1,0.5,0.5,1,0,0,0.5,1,1,1,0.5,0,0,1,1,0.5,0,0,0.5,0,1,0,1,0,0,0,1,1,0,0,1,0,0,0,0.5,0,0,1,0,0.5,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,0.5,1,0.5,0,1,0.5,1,0,0,0.5,0.5,0,0.5,0.5,0.5,1,0.5,0,0,0.5,0,0,1,1,1,1,0.5,1,0.5,0,1,0,0,1,0.5,0,0.5,1,1,0.5,1,0.5,0,0,0]
recall
0.4875 [0,1,0,1,1,0,1,0,0,0.5,0.5,0,0,0,1,1,0.5,0,0,0,0.5,0,0.5,1,1,0.5,0,0,1,0.5,1,1,0.5,0,1,0,0.5,0.5,1,0,0.5,1,0,0,1,1,0.5,0,0.5,0.5,0,0,0.5,0,1,0,1,0.5,0.5,1,0.5,1,1,1,0.5,0,1,1,1,0.5,1,0,0,1,0.5,0,1,0.5,0,0,0,0.5,0.5,0,0,1,1,1,1,0.5,0,0.5,0,1,0.5,1,0.5,1,0,1]
recall
0.49375 [0.5,1,1,1,1,0,0.5,0.5,1,1,0,0,0.5,0,1,0.5,0,1,0,0,0.5,1,0,0.5,0.5,0,0,0.5,1,1,0.5,0,0,0,0.5,0,0,0,0.5,0,0.5,0,1,0,0,1,0,0,1,1,0,0.5,1,0,0.5,0,0.5,0.5,0.5,1,0.5,1,1,0.5,0,0,0,1,0.5,1,1,0.5,0.5,0,0.5,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,0.5,1,0.5,0.5,0,1,0.5,0.5,0,0,0.5]
recall
0.50625 [0.5,0,1,0.5,0,1,0.5,0.5,1,0.5,0,1,0.5,1,0,0.5,0,0,0.5,0.5,0,1,0.5,1,0,0,1,0.5,0,1,0,1,1,0,0.5,0.5,0,0.5,0.5,1,1,0.5,1,1,0,1,0.5,0,0,0,0,0.5,0,0,0.5,0,1,1,0.5,0,0,0,1,1,0.5,0,0,0,0,0.5,1,0.5,0,1,0,1,1,1,1,1,0.5,1,0,1,0,0.5,1,1,1,1,0,1,1,1,1,1,0,0,0.5,0]
recall
0.4625 [1,0,1,1,0,0,0.5,0,0,1,0,1,1,0,1,1,0,0,0,0,0,1,0,0.5,1,0,1,1,0.5,1,1,0,1,0,0.5,1,0.5,0.5,0.5,0,1,1,0.5,0,0,0.5,0,0.5,0,0,0,0.5,0.5,0,0,0,0.5,0,1,0.5,0.5,0.5,0,0.5,1,1,0,0,0.5,0,1,0,0,0.5,0.5,0.5,0,1,1,1,0.5,0,1,0,0.5,1,1,0.5,1,0.5,0.5,0,0,0.5,1,1,1,0.5,0,0]
recall
0.48125 [1,0,1,1,1,0,1,1,0.5,1,1,1,0,0.5,1,0,0,0,0,0,0,0.5,0,0.5,0,0,0,1,0,0,1,0.5,1,1,1,0,0,0,0.5,0,1,1,0,0.5,0,1,1,0.5,1,0,1,0.5,0.5,0.5,0.5,1,1,0,0.5,1,0,0.5,0,0.5,0,0,0.5,1,0.5,0,1,1,0,0.5,0,0.5,1,0,0,0.5,0.5,0,0,0,0.5,0,0.5,1,1,1,0.5,1,0.5,1,1,0.5,0,0,0.5,0]
relative_absolute_error
0.7833969447438908
relative_absolute_error
0.7779764781785748
relative_absolute_error
0.8012671162747567
relative_absolute_error
0.8193799134848742
relative_absolute_error
0.7748840885778787
relative_absolute_error
0.8013222551171869
relative_absolute_error
0.8116464109950006
relative_absolute_error
0.8005386337724124
relative_absolute_error
0.7808455576605756
relative_absolute_error
0.8007119976753354
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
0.08412994687530786
root_mean_squared_error
0.08322904796122227
root_mean_squared_error
0.08493336282218565
root_mean_squared_error
0.0861709157069149
root_mean_squared_error
0.08288074248159837
root_mean_squared_error
0.08568754517692352
root_mean_squared_error
0.08609684953098935
root_mean_squared_error
0.08477290672923764
root_mean_squared_error
0.08428145272700405
root_mean_squared_error
0.0855289573631874
root_relative_squared_error
0.8455377800543294
root_relative_squared_error
0.8364834052905096
root_relative_squared_error
0.8536124141342739
root_relative_squared_error
0.8660502886096343
root_relative_squared_error
0.8329828035076693
root_relative_squared_error
0.8611922319954018
root_relative_squared_error
0.865305895533266
root_relative_squared_error
0.8519997697232589
root_relative_squared_error
0.8470604711562066
root_relative_squared_error
0.8595983644969611
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
1962.6054760000002
usercpu_time_millis
1734.5176350000004
usercpu_time_millis
1807.3303790000004
usercpu_time_millis
1792.5932159999993
usercpu_time_millis
1736.6713889999996
usercpu_time_millis
1821.3697339999992
usercpu_time_millis
1699.9004739999996
usercpu_time_millis
1810.1651099999997
usercpu_time_millis
1728.1233740000027
usercpu_time_millis
1784.982444000004
usercpu_time_millis_testing
100.16089900000003
usercpu_time_millis_testing
100.1164990000003
usercpu_time_millis_testing
99.15288500000052
usercpu_time_millis_testing
105.73705399999866
usercpu_time_millis_testing
96.8647960000002
usercpu_time_millis_testing
107.86137200000034
usercpu_time_millis_testing
100.62922899999904
usercpu_time_millis_testing
96.95341400000146
usercpu_time_millis_testing
102.9686500000011
usercpu_time_millis_testing
95.23296800000125
usercpu_time_millis_training
1862.4445770000002
usercpu_time_millis_training
1634.4011360000002
usercpu_time_millis_training
1708.177494
usercpu_time_millis_training
1686.8561620000007
usercpu_time_millis_training
1639.8065929999993
usercpu_time_millis_training
1713.508361999999
usercpu_time_millis_training
1599.2712450000006
usercpu_time_millis_training
1713.2116959999983
usercpu_time_millis_training
1625.1547240000016
usercpu_time_millis_training
1689.749476000003