5963443
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)
3998177
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
11699
6941
splitter
"best"
6941
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"
6948
n_values
"auto"
6948
sparse
false
6948
threshold
0.0
6949
algorithm
"SAMME"
6971
learning_rate
0.029511633562044875
6971
n_estimators
437
6971
random_state
30347
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
12850290
description
https://api.openml.org/data/download/12850290/description.xml
-1
12850289
predictions
https://api.openml.org/data/download/12850289/predictions.arff
area_under_roc_curve
0.984507575757576 [0.954467,0.994437,0.998264,0.999921,0.998737,0.991319,0.994003,0.993766,0.990609,0.997475,0.985795,0.993608,0.987413,0.967724,0.999724,0.956282,0.999171,0.981455,0.972025,0.974353,0.993292,0.994831,0.975063,0.999921,0.986979,0.959399,0.954467,0.987768,0.995344,0.999211,0.993371,0.998935,0.992819,0.981731,0.993884,0.996409,0.983941,0.976405,0.993647,0.998461,0.980074,0.989307,0.999211,0.985953,0.991872,0.994358,0.996252,0.992622,0.974116,0.99199,0.980035,0.990333,0.98765,0.95715,0.979167,0.970289,1,0.996843,0.992345,0.961332,0.997948,0.997711,0.986348,0.983546,0.99637,0.987255,0.961608,0.991556,0.966225,0.96587,0.960859,0.99708,0.983152,0.995739,0.93462,0.967172,0.998382,0.942116,0.999329,0.996567,0.958649,0.98986,0.977312,0.989741,0.974195,0.970407,1,0.985046,0.993292,0.99633,0.998658,0.998185,0.980271,0.972538,0.982086,0.987571,0.984296,0.981771,0.931029,0.99274]
average_cost
0
f_measure
0.6330048230982227 [0.4,0.685714,0.9375,0.969697,0.8,0.684211,0.823529,0.8125,0.580645,0.9375,0.461538,0.875,0.666667,0.461538,0.882353,0.521739,0.857143,0.424242,0.439024,0.206897,0.848485,0.8125,0.344828,0.969697,0.8,0.285714,0.2,0.647059,0.717949,0.9375,0.705882,0.933333,0.896552,0.4375,0.705882,0.702703,0.333333,0.25,0.727273,0.702703,0.705882,0.774194,0.731707,0.666667,0.785714,0.740741,0.685714,0.625,0.571429,0.733333,0.615385,0.5,0.533333,0.333333,0.647059,0.428571,1,0.8,0.625,0.32,0.909091,0.666667,0.347826,0.642857,0.756757,0.411765,0.292683,0.518519,0.424242,0.333333,0.6,0.636364,0.307692,0.848485,0.516129,0.714286,0.8,0.451613,0.914286,0.875,0.173913,0.645161,0.606061,0.571429,0.416667,0.6875,0.969697,0.606061,0.647059,0.882353,0.8125,0.736842,0.727273,0.444444,0.64,0.647059,0.416667,0.387097,0.32,0.785714]
kappa
0.639520202020202
kb_relative_information_score
0.2546832130000467
mean_absolute_error
0.019799853323482032
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
0.6469897187697362 [0.428571,0.631579,0.9375,0.941176,0.736842,0.590909,0.777778,0.8125,0.6,0.9375,0.6,0.875,0.538462,0.6,0.833333,0.857143,0.789474,0.411765,0.36,0.230769,0.823529,0.8125,0.384615,0.941176,0.736842,0.263158,0.214286,0.611111,0.608696,0.9375,0.666667,1,1,0.4375,0.666667,0.619048,0.357143,0.375,0.705882,0.619048,0.666667,0.8,0.6,0.6,0.916667,0.909091,0.631579,0.625,0.666667,0.785714,0.8,0.375,0.571429,0.357143,0.611111,0.5,1,0.736842,0.625,0.444444,0.882353,0.565217,0.571429,0.75,0.666667,0.388889,0.24,0.636364,0.411765,0.5,0.642857,0.5,0.4,0.823529,0.533333,0.833333,0.736842,0.466667,0.842105,0.875,0.285714,0.666667,0.588235,0.526316,0.625,0.6875,0.941176,0.588235,0.611111,0.833333,0.8125,0.636364,0.705882,0.545455,0.888889,0.611111,0.625,0.4,0.444444,0.916667]
predictive_accuracy
0.643125
prior_entropy
6.6438561897747395
recall
0.643125 [0.375,0.75,0.9375,1,0.875,0.8125,0.875,0.8125,0.5625,0.9375,0.375,0.875,0.875,0.375,0.9375,0.375,0.9375,0.4375,0.5625,0.1875,0.875,0.8125,0.3125,1,0.875,0.3125,0.1875,0.6875,0.875,0.9375,0.75,0.875,0.8125,0.4375,0.75,0.8125,0.3125,0.1875,0.75,0.8125,0.75,0.75,0.9375,0.75,0.6875,0.625,0.75,0.625,0.5,0.6875,0.5,0.75,0.5,0.3125,0.6875,0.375,1,0.875,0.625,0.25,0.9375,0.8125,0.25,0.5625,0.875,0.4375,0.375,0.4375,0.4375,0.25,0.5625,0.875,0.25,0.875,0.5,0.625,0.875,0.4375,1,0.875,0.125,0.625,0.625,0.625,0.3125,0.6875,1,0.625,0.6875,0.9375,0.8125,0.875,0.75,0.375,0.5,0.6875,0.3125,0.375,0.25,0.6875]
relative_absolute_error
0.9999925920950503
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.09949800664372713
root_relative_squared_error
0.9999925921985799
total_cost
0
area_under_roc_curve
0.9838719548602821 [0.981132,0.996835,1,1,1,1,1,1,0.993671,0.993711,0.968354,0.974684,0.990506,0.987342,1,0.924528,1,0.968354,0.958861,0.993711,1,0.981132,0.987421,1,1,0.981013,0.958861,0.996835,0.993671,1,0.962025,1,0.974684,0.993671,1,0.981132,0.993711,0.974843,1,1,1,1,0.996835,1,1,0.987342,0.993711,1,1,0.974684,0.927215,1,0.987421,0.971519,1,1,1,1,0.987421,0.962025,1,1,1,0.971519,0.993711,0.990506,0.987342,1,1,0.962264,0.946203,0.993711,0.977848,1,1,1,1,0.89557,1,1,0.974684,1,0.993711,1,0.946203,0.901899,1,0.996835,0.990506,0.993711,1,1,1,0.977848,0.996835,0.984177,1,0.990506,0.803797,1]
area_under_roc_curve
0.9867640912347746 [0.930818,0.993671,0.996835,1,1,0.987421,0.981013,1,1,1,1,0.981013,0.974684,0.949367,1,1,0.996835,0.981013,0.96519,0.981132,1,1,1,1,1,0.936709,0.993671,0.952532,1,1,1,1,1,0.987342,0.987342,0.987421,0.981132,0.993711,0.974843,1,1,1,1,0.990506,0.996835,0.996835,1,1,0.984177,1,1,0.96519,1,1,0.968553,0.962264,1,1,1,0.93038,1,0.993711,0.993671,0.955696,1,0.990506,0.996835,0.974843,0.886792,1,1,1,0.993671,1,1,0.990506,0.993671,0.93038,1,1,0.958861,0.993671,1,0.987421,0.996835,1,1,0.990506,1,1,0.990506,1,1,0.952532,0.996835,1,0.993711,0.943038,0.93038,1]
area_under_roc_curve
0.986375238834488 [0.981013,0.993671,1,1,0.996835,0.993671,1,1,0.987342,1,1,1,1,0.949367,1,0.987342,1,0.981013,0.943396,0.968354,0.993711,0.968553,0.93038,1,0.849057,0.977848,1,0.987421,1,1,0.987421,1,1,0.974684,1,1,1,0.918239,1,1,1,1,1,1,0.990506,0.981132,0.996835,1,0.905063,0.993671,1,0.993711,1,0.990506,1,0.990506,1,1,0.937107,0.974684,1,1,0.996835,1,0.996835,0.96519,0.96519,0.984177,0.987421,0.889241,0.990506,1,0.993711,0.987342,1,0.984177,1,0.984177,1,0.996835,0.993711,0.996835,0.993671,1,0.937107,1,1,1,0.996835,1,1,1,0.993711,0.958861,0.981013,1,0.984177,0.977848,0.968553,1]
area_under_roc_curve
0.9838888723031607 [0.946203,0.996835,1,1,0.996835,0.955696,1,0.993671,0.996835,1,0.981013,0.996835,1,0.990506,1,0.981013,1,1,0.874214,0.993671,1,1,1,1,0.987421,0.958861,0.851266,1,0.996835,1,1,1,1,0.987342,0.955975,1,1,0.981132,1,1,1,1,1,0.993711,1,1,1,1,0.987342,1,0.993671,1,0.937107,0.914557,1,0.971519,1,1,1,0.968354,1,1,0.990506,1,0.990506,1,0.952532,0.987342,1,0.971519,0.984177,0.993671,0.987421,1,0.930818,0.933544,1,0.946203,1,0.993671,0.823899,0.993671,0.981013,0.990506,0.968553,1,1,0.987342,1,1,1,1,0.943396,0.987342,1,0.981132,0.962025,0.987342,0.924528,0.974684]
area_under_roc_curve
0.9835734117506567 [0.993671,1,1,1,1,1,0.987421,0.996835,1,1,0.971519,1,1,0.823899,1,0.933544,1,0.987342,1,0.984177,0.981013,0.990506,1,1,0.990506,0.974684,0.987342,1,1,1,1,1,1,0.952532,1,1,0.987342,0.949367,0.996835,1,1,0.971519,1,0.993711,0.993711,0.993711,1,1,1,1,0.987421,0.974843,0.993671,1,0.879747,0.971519,1,0.987342,0.996835,0.918239,0.990506,1,0.987421,1,0.996835,0.996835,0.924528,0.996835,0.924051,0.977848,1,1,0.968553,0.993711,0.816456,1,1,0.962025,1,1,1,1,1,0.990506,0.899371,0.924528,1,1,1,1,1,0.993671,0.987421,0.955975,0.949367,0.993711,0.996835,0.993711,0.962264,0.984177]
area_under_roc_curve
0.9843543507682503 [0.958861,1,1,1,1,0.987342,1,0.990506,1,0.981013,0.990506,1,1,0.981132,1,0.943038,1,0.993671,1,0.943038,0.993671,0.990506,0.987342,1,1,0.96519,0.914557,1,1,1,1,1,0.971519,0.990506,0.981132,0.996835,0.981013,0.981013,0.993671,1,0.971519,0.981013,1,0.974843,0.981132,0.987421,1,0.981013,0.984177,0.977848,1,1,0.996835,1,0.987342,0.977848,1,0.996835,1,0.993711,1,0.996835,1,1,1,0.990506,0.90566,1,0.984177,0.993671,0.85443,0.993671,1,1,0.936709,0.981132,0.996835,0.933544,1,1,0.962264,0.987342,1,0.990506,0.987421,1,1,0.968553,0.946203,1,1,0.993671,0.90566,0.987421,0.993671,1,0.993671,0.974843,0.949686,0.993671]
area_under_roc_curve
0.9861080427513729 [0.987342,1,1,1,1,1,1,1,1,1,1,1,0.952532,0.987421,1,0.920886,1,0.981132,0.984177,0.933544,0.990506,1,0.996835,1,0.990506,0.880503,0.981132,0.981013,1,1,0.996835,1,1,0.962264,1,1,0.981013,1,1,0.981132,0.990506,0.996835,1,0.987342,0.949686,0.996835,0.993671,0.990506,0.993711,1,0.962264,0.993671,1,1,0.993671,0.984177,1,0.996835,0.993671,0.993711,1,1,1,0.971519,1,0.981132,0.924528,0.993671,0.958861,0.968354,1,1,1,1,0.987342,0.993711,1,0.81761,1,1,0.943038,1,0.936709,0.996835,1,0.993671,1,0.880503,1,0.996835,1,1,0.971519,0.955975,0.962264,1,1,1,0.943038,1]
area_under_roc_curve
0.9881513215508315 [0.981013,1,1,1,1,0.996835,1,1,0.987421,1,0.981132,1,0.974684,0.993711,1,0.987342,1,0.993711,0.971519,1,0.984177,1,0.996835,1,1,0.955975,0.981132,0.971519,0.968553,1,1,1,1,0.987421,0.996835,1,0.981013,0.958861,0.996835,1,1,1,1,0.984177,0.987421,0.996835,1,0.990506,0.974843,0.993711,0.987421,0.990506,0.981013,1,0.996835,0.946203,1,0.996835,0.981013,0.937107,1,1,0.987421,0.996835,0.996835,0.974843,0.981132,1,0.943038,0.946203,0.981132,1,0.974684,1,1,0.993711,1,0.981132,1,0.993711,0.958861,0.962264,0.971519,0.993671,0.990506,0.93038,1,1,1,0.993671,1,1,0.996835,0.968553,0.993711,0.955696,0.977848,0.987421,0.996835,1]
area_under_roc_curve
0.9854310962502986 [0.974843,0.993671,1,1,0.996835,0.981132,0.984177,0.981132,0.977848,1,0.993711,1,0.993671,0.990506,1,0.974843,1,1,1,1,1,1,0.90566,1,1,1,0.962264,1,0.993671,1,1,1,1,0.993711,1,1,1,0.984177,1,1,0.830189,1,1,0.971519,0.987342,0.990506,1,1,0.987421,1,0.987342,0.987342,0.996835,0.835443,1,0.924528,1,1,1,0.96519,1,1,1,1,1,0.993711,0.933544,0.987421,0.984177,0.974843,0.993711,0.987421,0.958861,1,0.936709,0.892405,1,0.974843,1,0.996835,0.952532,0.974843,1,0.987421,0.971519,1,1,0.996835,0.987421,1,1,0.996835,0.987342,0.993671,0.943396,0.968354,0.993711,1,0.977848,0.993711]
area_under_roc_curve
0.9868690788949925 [0.679245,0.990506,0.981132,1,1,1,1,0.968553,0.993671,1,0.987421,1,0.996835,0.990506,0.996835,0.943396,1,0.981132,0.987342,0.987421,0.996835,0.993671,1,1,1,0.943396,1,1,0.993671,1,1,0.990506,1,0.981132,0.990506,0.993671,0.968354,0.993671,0.984177,1,0.987421,0.981013,1,0.984177,0.996835,1,1,0.987342,0.993711,0.987421,0.996835,1,0.993671,0.958861,0.993671,0.943396,1,1,0.996835,0.971519,1,0.996835,0.968354,0.974684,1,0.993711,0.996835,0.993711,0.996835,0.993711,0.811321,1,0.981013,0.996835,0.936709,0.981013,1,1,0.987421,0.990506,0.996835,0.993711,0.893082,0.968553,0.993671,0.996835,1,1,1,0.993671,1,0.996835,0.987342,0.990506,0.993711,0.990506,0.968553,0.996835,0.990506,0.987421]
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
0.595720320581152
kappa
0.6462971735354492
kappa
0.6463111356728378
kappa
0.608354060562991
kappa
0.621002763521516
kappa
0.6462832102956851
kappa
0.6335202590632651
kappa
0.6778141903897027
kappa
0.6588620839420382
kappa
0.6588351431391904
kb_relative_information_score
0.02505954939859066
kb_relative_information_score
0.025020666741320516
kb_relative_information_score
0.025971689902703293
kb_relative_information_score
0.025402017128545763
kb_relative_information_score
0.026093916993601276
kb_relative_information_score
0.02523281870379694
kb_relative_information_score
0.026345559745135364
kb_relative_information_score
0.02611472949888684
kb_relative_information_score
0.02516796154313837
kb_relative_information_score
0.024274303344327643
mean_absolute_error
0.01979985567857383
mean_absolute_error
0.019799855902649478
mean_absolute_error
0.01979985042370681
mean_absolute_error
0.019799853706276856
mean_absolute_error
0.019799849718158413
mean_absolute_error
0.01979985468050613
mean_absolute_error
0.019799848267631127
mean_absolute_error
0.019799849598988783
mean_absolute_error
0.019799855054536557
mean_absolute_error
0.019799860203792756
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
0.6
predictive_accuracy
0.65
predictive_accuracy
0.65
predictive_accuracy
0.6125
predictive_accuracy
0.625
predictive_accuracy
0.65
predictive_accuracy
0.6375
predictive_accuracy
0.68125
predictive_accuracy
0.6625
predictive_accuracy
0.6625
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
0.6 [0,1,1,1,1,1,1,1,0,1,0,0.5,1,0,1,0,1,0,0.5,1,1,0,0,1,1,0.5,0,0.5,0.5,0,0.5,1,0.5,0,0.5,0,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,0,1,1,0,0,1,1,0,0.5,1,1,0.5,1,0,0,0,1,0,1,0,0.5,1,0.5,1,1,0,1,0,1,0,0.5,1,1,0.5,1,1,1,1,0.5,0.5,0.5,0,0,0,1]
recall
0.65 [0,0,1,1,1,1,0.5,1,1,1,0.5,1,0.5,0.5,1,1,0.5,0,0,0,1,1,0,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,0,0.5,1,1,1,0.5,1,1,0,1,0,1,1,0,1,0.5,0,0,1,1,1,0.5,1,1,0,0.5,1,0.5,0,0,0,0,1,1,1,1,1,0.5,0.5,0.5,1,1,0,0.5,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0.5,0.5,1,1,0,0,1]
recall
0.65 [0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0,1,0.5,1,0,0,0,0,1,0,0.5,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0,1,1,0,0,0.5,1,0.5,1,1,1,0,0.5,1,0,1,1,1,0,1,1,0,1,1,0.5,0.5,0.5,1,0.5,0.5,1,0,0.5,1,1,1,0.5,1,1,0,0.5,0.5,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,0,0,0,0,1]
recall
0.6125 [0,1,1,1,0.5,0,1,0.5,0.5,1,0.5,0.5,0,0.5,1,0.5,1,1,0,0,1,1,0.5,1,0,0,0,0,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,1,0,0,1,1,1,1,0,0,1,0,1,1,0.5,1,0.5,1,0,0.5,0.5,0.5,0,1,0,0.5,1,0.5,1,1,0,0.5,1,0.5,0,1,1,0.5,1,1,1,1,0,0,0.5,0,0,0.5,0,0]
recall
0.625 [0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0,1,0.5,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0,1,1,1,0,1,1,0,1,1,0.5,0,0.5,1,1,0.5,1,1,1,0,1,0.5,0.5,0.5,0,0,0.5,1,0.5,0.5,1,0.5,0.5,0,0.5,1,0,0,0.5,0,0,0,0.5,0,1,1,0,0,0.5,1,1,0,1,1,1,1,1,0.5,0,0,1,0,1,1,1,0.5,1,0,0.5,1,0.5,0,0,0.5]
recall
0.65 [0.5,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,0.5,1,0,1,0.5,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,0.5,0.5,1,0.5,0.5,0.5,0.5,1,0.5,0.5,0,1,1,1,1,0,0,0.5,1,1,0.5,0,0.5,0.5,1,1,0.5,1,1,0.5,0,1,1,0.5,0,1,0.5,0.5,0.5,1,1,1,0.5,1,0.5,0.5,1,1,0,0.5,1,0.5,0,1,1,0,0,0.5,1,1,0,1,0.5,1,0.5,0,0,1]
recall
0.6375 [1,1,1,1,1,1,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,0,1,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,0,1,1,0,0.5,1,0,0.5,0.5,1,1,0,0.5,0,0.5,1,1,0,1,1,0,0.5,0.5,1,0.5,0.5,0,1,1,0,0.5,1,0,0,0,0.5,0,1,1,0.5,1,0.5,1,1,0,1,1,0,1,0.5,1,0.5,0.5,1,0,0,1,1,1,0,0,0,0.5,0.5,1,0,1]
recall
0.68125 [1,1,1,1,1,0.5,1,1,0,1,0,1,1,0,1,0.5,1,1,0.5,0,1,1,0,1,1,0,0,0.5,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0,0,0.5,1,1,1,1,0,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,0,1,0.5,0,0.5,0,1,0,1,1,0,1,1,1,1,0,0,0,0.5,0,0.5,1,1,1,1,0,1,1,0,1,1,0,0,0.5,1]
recall
0.6625 [0,1,1,1,1,1,1,1,0,1,0,1,1,0.5,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,0,1,0.5,1,0,0,1,0,0,1,1,0.5,1,1,1,0.5,0,1,0.5,0.5,0,0.5,0.5,0,1,1,0.5,0,1,1,0.5,1,1,0,0.5,0,1,0,1,0,0,1,0.5,0,1,0,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0,0.5,0,1,0.5,0]
recall
0.6625 [0,1,0,1,1,1,0.5,0,1,1,1,1,1,0.5,0.5,0,1,0,0.5,0,1,1,1,1,1,0,1,0.5,1,1,1,0.5,1,1,1,0.5,0,0,0.5,1,0,1,1,1,0.5,0.5,1,0.5,0,1,0.5,1,1,0,1,0,1,1,1,0.5,1,0.5,0,0.5,1,0,0.5,0,0.5,0,0,1,0,1,0,1,1,1,1,0.5,0.5,0,0,0,0.5,0.5,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,0]
relative_absolute_error
0.9999927110390807
relative_absolute_error
0.9999927223560325
relative_absolute_error
0.9999924456417564
relative_absolute_error
0.9999926114281223
relative_absolute_error
0.999992410007999
relative_absolute_error
0.9999926606316211
relative_absolute_error
0.9999923367490452
relative_absolute_error
0.9999924039893309
relative_absolute_error
0.9999926795220466
relative_absolute_error
0.9999929395854911
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
0.099498018478403
root_mean_squared_error
0.09949801960438079
root_mean_squared_error
0.0994979920717561
root_mean_squared_error
0.09949800856739444
root_mean_squared_error
0.09949798852695926
root_mean_squared_error
0.09949801346302477
root_mean_squared_error
0.09949798123704534
root_mean_squared_error
0.09949798792745336
root_mean_squared_error
0.09949801534256722
root_mean_squared_error
0.09949804121827117
root_relative_squared_error
0.9999927111415481
root_relative_squared_error
0.9999927224580506
root_relative_squared_error
0.9999924457447608
root_relative_squared_error
0.9999926115321641
root_relative_squared_error
0.9999924101182122
root_relative_squared_error
0.9999926607351003
root_relative_squared_error
0.9999923368518207
root_relative_squared_error
0.9999924040929511
root_relative_squared_error
0.9999926796252125
root_relative_squared_error
0.9999929396858224
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
6588.338081999999
usercpu_time_millis
6508.274877
usercpu_time_millis
6499.5767319999995
usercpu_time_millis
6540.688597999999
usercpu_time_millis
6522.636812000002
usercpu_time_millis
6564.0784150000045
usercpu_time_millis
6520.362044999999
usercpu_time_millis
6507.040665999994
usercpu_time_millis
6357.688644
usercpu_time_millis
6471.551223999995
usercpu_time_millis_testing
119.26122399999883
usercpu_time_millis_testing
120.28717499999964
usercpu_time_millis_testing
118.89368399999967
usercpu_time_millis_testing
115.14319200000145
usercpu_time_millis_testing
123.53164299999975
usercpu_time_millis_testing
115.88005300000503
usercpu_time_millis_testing
115.17252699999858
usercpu_time_millis_testing
118.27155399999612
usercpu_time_millis_testing
118.53223600000007
usercpu_time_millis_testing
126.80702100000474
usercpu_time_millis_training
6469.076858
usercpu_time_millis_training
6387.987702
usercpu_time_millis_training
6380.683048
usercpu_time_millis_training
6425.5454059999975
usercpu_time_millis_training
6399.105169000002
usercpu_time_millis_training
6448.198361999999
usercpu_time_millis_training
6405.189518
usercpu_time_millis_training
6388.769111999998
usercpu_time_millis_training
6239.156408
usercpu_time_millis_training
6344.74420299999