6018267
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)
4052955
bootstrap
true
6902
class_weight
null
6902
criterion
"entropy"
6902
max_depth
null
6902
max_features
0.14881114750042848
6902
max_leaf_nodes
null
6902
min_impurity_split
1e-07
6902
min_samples_leaf
14
6902
min_samples_split
20
6902
min_weight_fraction_leaf
0.0
6902
n_estimators
100
6902
n_jobs
1
6902
oob_score
false
6902
random_state
67
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
12959659
description
https://api.openml.org/data/download/12959659/description.xml
-1
12959660
predictions
https://api.openml.org/data/download/12959660/predictions.arff
area_under_roc_curve
0.9949183238636364 [0.98544,0.999842,1,1,0.999527,0.995068,0.999487,0.999092,0.995068,0.999921,0.995896,0.999842,0.998816,0.981889,0.999487,0.998264,0.999842,0.986072,0.989741,0.97672,0.997475,0.99929,0.992898,1,0.99712,0.969934,0.985795,0.999527,0.996409,0.999684,0.999921,1,0.99925,0.9884,0.999171,0.997909,0.994752,0.988913,0.999329,0.999921,0.998658,0.997988,0.999921,0.998422,0.998146,0.999724,0.99929,0.995699,0.998067,0.997356,0.997633,0.994437,0.997041,0.984336,0.994239,0.972854,1,0.999921,0.991832,0.977825,0.999645,0.999408,0.991951,0.998777,0.999014,0.993845,0.979403,1,0.993253,0.986427,0.996607,0.998106,0.992424,0.997909,0.97676,0.999329,0.998816,0.983625,1,0.998935,0.99416,0.996449,0.998106,0.99562,0.995028,0.999369,1,0.995541,0.995818,0.999921,0.999842,0.999171,0.997672,0.990846,0.999014,0.999605,0.997238,0.995226,0.960622,0.99925]
average_cost
0
f_measure
0.752599293016417 [0.5,0.914286,0.969697,1,0.914286,0.571429,0.909091,0.882353,0.727273,0.888889,0.740741,0.914286,0.820513,0.521739,0.888889,0.903226,0.820513,0.48,0.5,0.25,0.717949,1,0.774194,1,0.83871,0.105263,0.333333,0.8,0.933333,0.842105,0.882353,1,0.941176,0.444444,0.9375,0.653061,0.588235,0.424242,0.882353,0.820513,0.777778,0.903226,1,0.810811,0.896552,0.833333,0.857143,0.758621,0.882353,0.823529,0.787879,0.689655,0.8,0.72,0.642857,0.190476,1,0.9375,0.482759,0.380952,0.909091,0.8125,0.583333,0.875,0.736842,0.6875,0.228571,0.914286,0.615385,0.75,0.6875,0.789474,0.536585,0.9375,0.583333,0.969697,0.888889,0.210526,0.941176,0.933333,0.64,0.764706,0.827586,0.685714,0.666667,0.875,1,0.882353,0.722222,0.933333,0.914286,0.714286,0.857143,0.56,0.882353,0.83871,0.733333,0.722222,0.380952,0.857143]
kappa
0.7657828282828282
kb_relative_information_score
924.1829191810248
mean_absolute_error
0.01682356480981356
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
0.7754360820137367 [0.75,0.842105,0.941176,1,0.842105,0.666667,0.882353,0.833333,0.705882,0.8,0.909091,0.842105,0.695652,0.857143,0.8,0.933333,0.695652,0.666667,0.45,0.25,0.608696,1,0.8,1,0.866667,0.333333,0.5,0.666667,1,0.727273,0.833333,1,0.888889,0.545455,0.9375,0.484848,0.555556,0.411765,0.833333,0.695652,0.7,0.933333,1,0.714286,1,0.75,0.789474,0.846154,0.833333,0.777778,0.764706,0.769231,0.736842,1,0.75,0.4,1,0.9375,0.538462,0.8,0.882353,0.8125,0.875,0.875,0.636364,0.6875,0.210526,0.842105,0.8,0.75,0.6875,0.681818,0.44,0.9375,0.875,0.941176,0.8,0.666667,0.888889,1,0.888889,0.722222,0.923077,0.631579,0.714286,0.875,1,0.833333,0.65,1,0.842105,0.576923,1,0.777778,0.833333,0.866667,0.785714,0.65,0.8,0.789474]
predictive_accuracy
0.768125
prior_entropy
6.6438561897747395
recall
0.768125 [0.375,1,1,1,1,0.5,0.9375,0.9375,0.75,1,0.625,1,1,0.375,1,0.875,1,0.375,0.5625,0.25,0.875,1,0.75,1,0.8125,0.0625,0.25,1,0.875,1,0.9375,1,1,0.375,0.9375,1,0.625,0.4375,0.9375,1,0.875,0.875,1,0.9375,0.8125,0.9375,0.9375,0.6875,0.9375,0.875,0.8125,0.625,0.875,0.5625,0.5625,0.125,1,0.9375,0.4375,0.25,0.9375,0.8125,0.4375,0.875,0.875,0.6875,0.25,1,0.5,0.75,0.6875,0.9375,0.6875,0.9375,0.4375,1,1,0.125,1,0.875,0.5,0.8125,0.75,0.75,0.625,0.875,1,0.9375,0.8125,0.875,1,0.9375,0.75,0.4375,0.9375,0.8125,0.6875,0.8125,0.25,0.9375]
relative_absolute_error
0.8496749903946226
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08671424552327625
root_relative_squared_error
0.871510958725644
total_cost
0
area_under_roc_curve
0.9947045915930263 [1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,0.987421,1,0.981013,0.974684,0.987421,1,1,1,1,1,0.984177,0.993671,1,0.993671,0.987421,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,0.993711,1,1,0.984177,0.996835,1,1,1,0.993711,1,1,1,0.987421,0.981013,1,1,0.987342,1,1,1,0.990506,1,1,0.943396,0.996835,1,1,1,1,1,1,0.977848,1,1,0.996835,1,1,1,0.984177,1,1,1,0.981013,1,1,1,1,0.990506,0.996835,1,1,0.996835,0.876582,1]
area_under_roc_curve
0.9943888822545975 [0.968553,1,1,1,1,1,1,1,1,1,0.993671,1,0.993671,0.933544,1,1,1,0.981013,0.996835,0.981132,1,1,1,1,1,0.971519,0.990506,1,1,1,1,1,1,1,0.996835,0.993711,0.993711,1,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.971519,1,1,1,0.962264,1,1,1,0.908228,1,1,0.993671,0.996835,1,1,0.984177,1,0.974843,1,1,1,1,1,1,1,1,0.977848,1,1,0.993671,0.996835,0.993711,0.981132,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.984177,0.990506,1]
area_under_roc_curve
0.9944274440729242 [1,1,1,1,0.996835,0.993671,1,1,0.987342,1,1,1,1,0.993671,0.993671,1,1,0.990506,0.987421,0.936709,0.993711,0.993711,0.939873,1,1,0.968354,1,1,0.996835,1,1,1,0.996835,0.974684,1,1,0.993711,0.962264,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,0.974684,1,1,1,1,1,0.974684,0.990506,1,0.993711,0.955696,1,0.996835,0.993711,1,1,1,1,0.990506,1,1,1,0.993671,1,1,0.993711,1,1,1,0.996835,1,1,1,0.993711,0.987342,1,1,1,0.993671,0.981132,1]
area_under_roc_curve
0.9955350390096329 [0.990506,0.996835,1,1,1,0.996835,1,1,0.993671,1,1,1,1,0.987342,1,1,1,1,0.943396,1,1,1,1,1,0.993711,0.955696,0.984177,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,0.974684,1,1,1,0.987342,1,1,0.996835,1,0.996835,0.993671,0.984177,1,1,0.981013,0.993671,0.996835,0.974843,1,0.993711,0.996835,1,0.987342,1,1,0.987421,0.990506,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.993711,0.987342,0.996835,1,0.996835]
area_under_roc_curve
0.9935226096648353 [0.990506,1,1,1,1,1,1,1,1,1,0.990506,0.996835,1,0.937107,1,1,1,1,1,0.990506,0.968354,1,1,1,1,0.981013,0.987342,1,1,1,1,1,0.996835,0.971519,1,1,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.990506,0.974843,0.96519,0.977848,1,0.996835,0.996835,0.993711,1,1,0.981132,1,1,1,0.924528,1,0.984177,0.996835,1,1,0.993711,1,0.886076,1,0.993671,0.984177,1,1,1,1,0.996835,0.993671,0.981132,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,0.987421,0.993671]
area_under_roc_curve
0.9951795239232546 [0.968354,1,1,1,1,0.984177,1,1,1,1,0.990506,1,1,0.987421,1,1,1,1,1,0.933544,1,0.993671,1,1,0.996835,0.987342,0.971519,1,1,1,1,1,0.996835,0.996835,0.993711,0.996835,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.993671,0.955696,1,1,0.987342,1,1,1,0.987421,1,1,0.993671,1,1,1,1,0.993671,0.990506,1,1,0.984177,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.987421,0.930818,0.996835]
area_under_roc_curve
0.995856470424329 [1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,1,0.993711,0.993671,0.974684,0.996835,1,1,1,0.993671,0.918239,0.987421,1,1,1,1,1,1,0.987421,1,1,0.996835,1,1,1,1,0.993671,1,1,0.993711,0.996835,0.996835,0.987342,1,1,1,1,1,1,0.993671,0.977848,1,1,0.987342,0.993711,0.996835,1,1,0.990506,0.996835,0.987421,0.974843,1,0.990506,1,1,1,1,1,1,1,1,0.968553,1,1,0.981013,1,1,1,1,1,1,0.955975,1,1,1,1,0.993671,1,1,1,1,1,0.955696,1]
area_under_roc_curve
0.9958564704243292 [1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,0.987342,1,0.974843,0.990506,0.990506,1,1,1,1,0.993671,0.968553,0.987421,1,0.981132,0.996835,1,1,1,0.993711,1,1,0.996835,0.981013,1,1,1,1,1,1,0.993711,1,1,0.990506,1,0.987421,0.974843,1,1,1,1,0.962025,1,1,0.993671,1,1,1,1,1,1,0.968553,0.962264,1,0.990506,0.990506,1,1,1,0.993711,0.993671,1,1,0.993711,1,1,0.990506,1,0.990506,1,0.990506,1,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.987342,1,0.977848,1]
area_under_roc_curve
0.9960114640554091 [0.981132,1,1,1,1,0.993711,1,1,0.996835,1,0.993711,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.993711,1,1,0.996835,0.96519,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,1,0.993671,0.996835,0.943038,0.996835,0.937107,1,1,0.993671,0.971519,0.996835,1,1,0.993671,1,1,0.996835,1,0.996835,0.993711,1,0.993711,0.981013,0.987342,1,1,1,0.993711,1,0.996835,0.996835,0.993711,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.987421,1,0.984177,1]
area_under_roc_curve
0.996882712363665 [0.949686,1,1,1,1,1,1,0.974843,0.993671,1,1,1,0.996835,0.990506,1,1,1,0.968553,0.993671,0.981132,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.993671,1,0.993671,1,1,1,1,0.987342,1,1,1,0.981013,1,0.993711,0.993711,0.996835,1,1,1,0.987421,1,0.981013,1,0.990506,1,1,1,1,1,1,0.974843,1,0.981132,0.996835,1,1,0.993671,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1]
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
0.7284710711184781
kappa
0.7789619104006316
kappa
0.7663127146409822
kappa
0.7283745903904615
kappa
0.7852094602598018
kappa
0.7852179406190777
kappa
0.7662758103359786
kappa
0.7915185974887466
kappa
0.7600442023837714
kappa
0.7662296635602591
kb_relative_information_score
91.31086715014611
kb_relative_information_score
92.03722732479142
kb_relative_information_score
91.43708184324609
kb_relative_information_score
92.6753217842159
kb_relative_information_score
92.50934311546808
kb_relative_information_score
92.41573877259485
kb_relative_information_score
93.12180340264739
kb_relative_information_score
92.87445639523496
kb_relative_information_score
92.7553447290902
kb_relative_information_score
93.04573466358814
mean_absolute_error
0.01688553495747075
mean_absolute_error
0.016841884793088745
mean_absolute_error
0.016883980093425847
mean_absolute_error
0.01681843546169818
mean_absolute_error
0.016761544674351876
mean_absolute_error
0.016836854722287325
mean_absolute_error
0.016768009295241568
mean_absolute_error
0.016768694480574425
mean_absolute_error
0.016836515241310954
mean_absolute_error
0.01683419437868582
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
0.73125
predictive_accuracy
0.78125
predictive_accuracy
0.76875
predictive_accuracy
0.73125
predictive_accuracy
0.7875
predictive_accuracy
0.7875
predictive_accuracy
0.76875
predictive_accuracy
0.79375
predictive_accuracy
0.7625
predictive_accuracy
0.76875
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
0.73125 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0.5,1,0,1,1,0,0,1,1,0,1,1,0.5,0.5,1,0,0,0.5,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,1,0,1,1,0,1]
recall
0.78125 [0,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,0,1]
recall
0.76875 [0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0.5,1,0,0,1,0,1,1,0.5,0,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,0,0,1,1,0.5,1,1,0,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1]
recall
0.73125 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0,0,0.5,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,0,1,1,0.5,1,0.5,0.5,0,1,0,1,0,1,0,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,0,1,1,1,1,1,1,1,0,1,0.5,0,0.5,1,0,0.5]
recall
0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,0,0,1,1,0.5,1,1,1,0,1,0.5,1,0,1,0.5,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1]
recall
0.7875 [0,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,0,1]
recall
0.76875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0,1,1,1,1,0.5,0,0,1,1,1,0.5,1,1,0,1,1,0.5,1,1,1,1,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,0.5,0,0.5,0,1,0.5,1,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1,1,1,1,1,0,1]
recall
0.79375 [0.5,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0.5,1,0,0.5,0,1,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,1,0,1,0,1,0,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0,1]
recall
0.7625 [0,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0.5,0.5,0,1,1,0,0,1,1,0,0.5,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1]
recall
0.76875 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0,1,1,0.5,0,1,1,0,1,1,1,0.5,1,0.5,1,0,0,0.5,1,0,1,1,0,1,1,1,0,1,0,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1]
relative_absolute_error
0.8528047958318545
relative_absolute_error
0.8506002420751878
relative_absolute_error
0.8527262673447383
relative_absolute_error
0.8494159324089976
relative_absolute_error
0.8465426603208005
relative_absolute_error
0.8503461980953181
relative_absolute_error
0.8468691563253303
relative_absolute_error
0.8469037616451714
relative_absolute_error
0.8503290525914609
relative_absolute_error
0.8502118373073632
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
0.08701729177459605
root_mean_squared_error
0.08674284059366635
root_mean_squared_error
0.08704877834339618
root_mean_squared_error
0.08669525885509777
root_mean_squared_error
0.08647873576070692
root_mean_squared_error
0.08678989986323458
root_mean_squared_error
0.08644919146512434
root_mean_squared_error
0.08644685632699589
root_mean_squared_error
0.08674224038675932
root_mean_squared_error
0.0867290043035668
root_relative_squared_error
0.8745566881491337
root_relative_squared_error
0.8717983499963649
root_relative_squared_error
0.8748731400723025
root_relative_squared_error
0.8713201355305928
root_relative_squared_error
0.8691439965531953
root_relative_squared_error
0.8722713134511102
root_relative_squared_error
0.8688470652103384
root_relative_squared_error
0.868823596189109
root_relative_squared_error
0.8717923176899792
root_relative_squared_error
0.8716592900486353
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
1424.0809889997763
usercpu_time_millis
1267.849130000286
usercpu_time_millis
1273.9489690002301
usercpu_time_millis
1268.6637440001505
usercpu_time_millis
1265.2709610001693
usercpu_time_millis
1270.7248910001
usercpu_time_millis
1274.4365280004786
usercpu_time_millis
1278.133341999819
usercpu_time_millis
1269.6478880006907
usercpu_time_millis
1270.079171999896
usercpu_time_millis_testing
34.25661900018895
usercpu_time_millis_testing
32.947992000117665
usercpu_time_millis_testing
33.00683999987086
usercpu_time_millis_testing
32.561248000092746
usercpu_time_millis_testing
32.53676199983602
usercpu_time_millis_testing
32.56759400028386
usercpu_time_millis_testing
32.63373400022829
usercpu_time_millis_testing
32.46321799997531
usercpu_time_millis_testing
32.66354800052795
usercpu_time_millis_testing
32.619235000311164
usercpu_time_millis_training
1389.8243699995874
usercpu_time_millis_training
1234.9011380001684
usercpu_time_millis_training
1240.9421290003593
usercpu_time_millis_training
1236.1024960000577
usercpu_time_millis_training
1232.7341990003333
usercpu_time_millis_training
1238.157296999816
usercpu_time_millis_training
1241.8027940002503
usercpu_time_millis_training
1245.6701239998438
usercpu_time_millis_training
1236.9843400001628
usercpu_time_millis_training
1237.459936999585