10016024
6892
Scikit-learn Bot
9954
Supervised Classification
8918
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)
7940745
n_jobs
null
8891
remainder
"passthrough"
8891
sparse_threshold
0.3
8891
transformer_weights
null
8891
memory
null
8892
axis
0
8893
copy
true
8893
missing_values
"NaN"
8893
strategy
"most_frequent"
8893
verbose
0
8893
copy
true
8894
with_mean
true
8894
with_std
true
8894
memory
null
8895
copy
true
8896
fill_value
-1
8896
missing_values
NaN
8896
strategy
"constant"
8896
verbose
0
8896
categorical_features
null
8897
categories
null
8897
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
8897
handle_unknown
"ignore"
8897
n_values
null
8897
sparse
true
8897
threshold
0.0
8898
memory
null
8918
bootstrap
true
8919
class_weight
null
8919
criterion
"gini"
8919
max_depth
null
8919
max_features
0.9951597585803429
8919
max_leaf_nodes
null
8919
min_impurity_decrease
0.0
8919
min_impurity_split
null
8919
min_samples_leaf
10
8919
min_samples_split
3
8919
min_weight_fraction_leaf
0.0
8919
n_estimators
100
8919
n_jobs
null
8919
oob_score
false
8919
random_state
33667
8919
verbose
0
8919
warm_start
false
8919
openml-python
Sklearn_0.20.2.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
20952571
description
https://api.openml.org/data/download/20952571/description.xml
-1
20952572
predictions
https://api.openml.org/data/download/20952572/predictions.arff
area_under_roc_curve
0.9943777619949492 [0.989544,0.999448,1,1,0.999684,0.993845,0.998777,0.998974,0.996488,0.999961,0.998185,0.999487,0.998737,0.988084,0.999724,0.999487,0.999921,0.986585,0.988913,0.972972,0.999882,0.999961,0.994949,1,0.998224,0.970683,0.972854,0.997711,0.998027,0.999487,0.998895,0.999605,0.994437,0.988834,0.998619,0.995739,0.994239,0.981652,0.998224,0.999566,0.998856,0.998935,0.998777,0.998461,0.998461,0.999684,0.997238,0.998067,0.99637,0.994989,0.998027,0.990964,0.994673,0.981218,0.995699,0.982718,0.999961,0.998974,0.98986,0.984217,0.999803,0.998895,0.951783,0.995107,0.996607,0.995541,0.98765,0.998816,0.98256,0.993095,0.992582,0.996843,0.995107,0.999961,0.977075,0.997396,0.999171,0.988321,0.999211,0.998658,0.9927,0.99562,0.996922,0.995818,0.994594,0.997356,0.999961,0.994003,0.999211,0.999961,0.998935,0.998816,0.99708,0.992306,0.997988,0.996725,0.997514,0.991319,0.971157,0.999053]
average_cost
0
f_measure
0.7312305989263116 [0.62069,0.833333,1,1,0.857143,0.774194,0.882353,0.774194,0.827586,0.969697,0.764706,0.833333,0.833333,0.615385,0.969697,0.933333,0.864865,0.588235,0.571429,0,0.83871,0.909091,0.733333,1,0.727273,0.347826,0.242424,0.666667,0.848485,0.903226,0.875,0.866667,0.8,0.428571,0.882353,0.65,0.666667,0.529412,0.8125,0.903226,0.810811,0.909091,0.717949,0.742857,0.774194,0.896552,0.727273,0.8,0.709677,0.705882,0.875,0.588235,0.777778,0.583333,0.685714,0.470588,0.969697,0.774194,0.26087,0.4,0.914286,0.744186,0.571429,0.789474,0.75,0.7,0.4375,0.733333,0.5,0.518519,0.47619,0.764706,0.5,0.967742,0.363636,0.72,0.777778,0.666667,0.941176,0.8,0.5,0.647059,0.774194,0.594595,0.6875,0.774194,1,0.702703,0.848485,1,0.842105,0.833333,0.83871,0.666667,0.933333,0.787879,0.8,0.592593,0.538462,0.8]
kappa
0.7392676767676767
kb_relative_information_score
1087.1895160001407
mean_absolute_error
0.014589390335277251
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
0.7476041160764375 [0.692308,0.75,1,1,0.789474,0.8,0.833333,0.8,0.923077,0.941176,0.722222,0.75,0.75,0.8,0.941176,1,0.761905,0.555556,0.526316,0,0.866667,0.882353,0.785714,1,0.705882,0.571429,0.235294,0.647059,0.823529,0.933333,0.875,0.928571,0.736842,0.5,0.833333,0.541667,0.647059,0.5,0.8125,0.933333,0.714286,0.882353,0.608696,0.684211,0.8,1,0.705882,0.857143,0.733333,0.666667,0.875,0.555556,0.7,0.875,0.631579,0.444444,0.941176,0.8,0.428571,0.555556,0.842105,0.592593,0.526316,0.681818,0.625,0.583333,0.4375,0.785714,0.75,0.636364,1,0.722222,0.45,1,0.666667,1,0.7,1,0.888889,0.857143,0.75,0.611111,0.8,0.52381,0.6875,0.8,1,0.619048,0.823529,1,0.727273,0.75,0.866667,0.714286,1,0.764706,0.857143,0.727273,0.7,0.736842]
predictive_accuracy
0.741875
prior_entropy
6.6438561897747395
recall
0.741875 [0.5625,0.9375,1,1,0.9375,0.75,0.9375,0.75,0.75,1,0.8125,0.9375,0.9375,0.5,1,0.875,1,0.625,0.625,0,0.8125,0.9375,0.6875,1,0.75,0.25,0.25,0.6875,0.875,0.875,0.875,0.8125,0.875,0.375,0.9375,0.8125,0.6875,0.5625,0.8125,0.875,0.9375,0.9375,0.875,0.8125,0.75,0.8125,0.75,0.75,0.6875,0.75,0.875,0.625,0.875,0.4375,0.75,0.5,1,0.75,0.1875,0.3125,1,1,0.625,0.9375,0.9375,0.875,0.4375,0.6875,0.375,0.4375,0.3125,0.8125,0.5625,0.9375,0.25,0.5625,0.875,0.5,1,0.75,0.375,0.6875,0.75,0.6875,0.6875,0.75,1,0.8125,0.875,1,1,0.9375,0.8125,0.625,0.875,0.8125,0.75,0.5,0.4375,0.875]
relative_absolute_error
0.7368378957210718
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07801819924430729
root_relative_squared_error
0.7841124051895645
total_cost
0
area_under_roc_curve
0.992883727410238 [0.993711,1,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,1,0.990506,1,1,1,0.984177,0.981013,0.993711,1,1,0.987421,1,0.993711,0.993671,0.911392,1,1,1,0.990506,1,0.974684,0.990506,1,0.968553,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,1,1,0.984177,1,1,1,1,0.981132,0.987342,1,1,1,0.990506,1,0.996835,0.993671,1,1,1,0.977848,1,1,1,1,1,1,0.977848,1,1,0.993671,0.996835,0.987421,1,0.984177,0.996835,1,1,0.993671,1,1,1,1,0.990506,1,0.984177,1,0.990506,0.876582,1]
area_under_roc_curve
0.9936387926916647 [0.981132,1,1,1,1,0.993711,0.996835,1,1,1,1,0.996835,0.993671,0.977848,1,1,1,0.96519,0.987342,0.987421,1,1,1,1,1,0.949367,0.987342,0.993671,1,1,1,1,1,1,1,0.981132,0.981132,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.952532,1,1,0.993711,0.962264,1,1,1,0.936709,1,1,0.996835,0.981013,1,1,1,1,0.949686,1,1,1,1,1,1,1,0.996835,0.971519,1,0.996835,0.981013,0.996835,0.993711,0.993711,0.996835,1,1,0.996835,1,1,0.993671,1,1,0.987342,1,1,1,0.971519,0.987342,1]
area_under_roc_curve
0.9960905779794601 [1,1,1,1,1,0.993671,0.993711,1,1,1,1,1,1,0.996835,1,1,1,0.990506,1,0.946203,1,1,0.996835,1,1,0.987342,1,1,1,1,1,1,1,0.977848,1,1,0.993711,0.955975,1,1,0.996835,1,1,1,0.996835,1,0.990506,1,0.996835,1,0.996835,1,1,1,1,0.996835,1,1,0.955975,0.993671,1,1,0.993671,1,0.996835,0.993671,0.990506,1,0.981132,0.993671,0.996835,0.996835,1,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.977848,1,1,1,0.990506,0.949686,1]
area_under_roc_curve
0.9962463179683146 [0.987342,0.996835,1,1,1,0.987342,1,1,0.990506,1,1,1,1,0.996835,1,1,1,1,0.955975,0.984177,1,1,1,1,0.993711,0.990506,0.962025,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.936709,1,0.990506,1,1,1,1,1,1,0.990506,1,0.996835,1,0.984177,0.993671,0.993711,1,1,0.996835,0.987421,1,0.987421,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.987421,1,1,0.993711,0.987342,0.996835,1,1]
area_under_roc_curve
0.9938355823580926 [0.996835,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.943396,1,1,1,0.996835,1,0.981013,1,1,1,1,1,0.996835,0.977848,1,1,1,1,1,0.996835,0.971519,1,1,1,0.933544,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.996835,0.993711,0.987342,0.974684,1,0.996835,0.996835,1,1,1,1,1,0.987342,1,0.962264,1,0.981013,0.971519,0.996835,1,0.993711,1,0.905063,1,1,0.981013,1,1,1,1,1,0.993671,0.981132,0.993711,1,1,1,1,1,0.996835,1,0.993711,0.984177,1,0.996835,1,1,0.996835]
area_under_roc_curve
0.9957701417084628 [0.96519,1,1,1,1,0.984177,1,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.946203,1,1,0.996835,1,1,0.996835,0.968354,1,1,1,1,1,0.987342,0.993671,1,0.993671,0.987342,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,0.987342,1,1,0.990506,0.993711,1,1,0.987421,1,1,1,0.993711,1,0.987342,0.996835,0.987342,0.990506,1,1,0.984177,1,1,0.993671,1,1,1,0.987342,1,0.996835,1,1,1,0.993711,0.996835,1,1,0.996835,0.987421,1,1,1,1,0.981132,1,1]
area_under_roc_curve
0.9970003881060423 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,0.949367,1,1,0.993671,1,0.993671,0.918239,0.987421,0.993671,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.987421,0.996835,0.993671,0.996835,0.993711,1,1,1,1,1,0.990506,1,1,1,0.993671,1,1,1,1,0.993671,1,1,0.993711,0.996835,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,0.962264,1,1,1,1,0.990506,1,1,1,1,1,0.990506,1]
area_under_roc_curve
0.9951046393599231 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.993711,0.996835,1,0.987421,0.981013,0.987342,1,1,1,1,1,0.981132,0.987421,0.996835,0.968553,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,0.993711,0.996835,1,1,0.993711,1,0.993711,0.993671,0.981013,1,0.993671,0.968354,1,1,0.974684,0.968553,1,0.996835,1,1,1,0.937107,0.993711,1,0.974684,0.977848,1,1,1,1,0.993671,1,1,1,1,1,0.993671,0.987421,0.984177,1,0.996835,0.984177,1,0.993711,1,1,1,1,1,0.987421,1,0.993671,0.993671,0.993711,0.987342,1]
area_under_roc_curve
0.9956967498606797 [0.981132,1,1,1,1,0.993711,0.996835,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.955975,1,1,0.974843,0.993711,1,1,1,1,1,1,1,1,1,1,0.962025,1,1,0.993711,1,0.996835,1,1,1,1,1,0.993711,1,0.996835,0.990506,1,0.946203,1,0.955975,1,1,1,0.981013,1,1,1,1,1,1,0.993671,1,0.996835,0.993711,1,0.974843,0.984177,1,0.996835,0.984177,1,0.993711,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,0.996835,1,1,0.987342,1,0.987342,0.993711,1,0.977848,1]
area_under_roc_curve
0.9928513852400286 [0.987421,1,1,1,1,0.993711,1,0.974843,0.996835,1,1,1,0.996835,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,0.880503,0.993711,0.996835,0.996835,1,1,0.996835,1,1,0.990506,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,1,0.996835,1,1,0.974684,1,1,1,0.993711,0.996835,0.993671,1,0.996835,0.724684,0.993671,1,1,0.993671,1,0.996835,0.993711,0.955975,1,0.987342,1,0.990506,0.990506,1,1,1,0.993671,1,0.981132,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
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
0.6967543236199952
kappa
0.7284281992579142
kappa
0.7915679772619612
kappa
0.722079665232324
kappa
0.7536031589338598
kappa
0.7409674234945706
kappa
0.7978282329713722
kappa
0.7157408504086226
kappa
0.7283209603538146
kappa
0.7157857340228161
kb_relative_information_score
106.71561150529767
kb_relative_information_score
107.79023094170374
kb_relative_information_score
109.27973161102933
kb_relative_information_score
109.43095625695159
kb_relative_information_score
108.47413558661705
kb_relative_information_score
109.02722552398117
kb_relative_information_score
110.9610032733982
kb_relative_information_score
108.09954188510591
kb_relative_information_score
109.0319064803141
kb_relative_information_score
108.3791729357429
mean_absolute_error
0.014718447261378267
mean_absolute_error
0.01463019653181396
mean_absolute_error
0.014553887741981191
mean_absolute_error
0.014599375565370557
mean_absolute_error
0.014488134631211003
mean_absolute_error
0.014609611323156043
mean_absolute_error
0.014337888977278956
mean_absolute_error
0.014638863164198292
mean_absolute_error
0.014653301206118782
mean_absolute_error
0.01466419695026537
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
0.7
predictive_accuracy
0.73125
predictive_accuracy
0.79375
predictive_accuracy
0.725
predictive_accuracy
0.75625
predictive_accuracy
0.74375
predictive_accuracy
0.8
predictive_accuracy
0.71875
predictive_accuracy
0.73125
predictive_accuracy
0.71875
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
0.7 [1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0,1,1,1,0,0.5,0,1,1,0,1,0,0.5,0,1,0.5,0,0.5,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,0,0,1,0.5,1,0,1,0.5,0.5,1,1,0,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,0.5,0.5,1,0.5,0,1]
recall
0.73125 [1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,0.5,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,0.5,1,0,1,1,0,0.5,1,1,1,0.5,1,1,0,1,0,0,0,1,1,0.5,0,0.5,0.5,0.5,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1]
recall
0.79375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,0,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,0,0,1,1,0.5,1,1,1,0.5,0.5,0,0.5,0,1,1,1,1,0.5,1,0.5,1,1,0,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1]
recall
0.725 [0.5,0.5,1,1,0.5,0,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0,0,0,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,0.5,0,0.5,0,1,0,0.5,1,0.5,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,0.5,1,0.5]
recall
0.75625 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,1,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,0.5,0.5,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,0,0,1,1,1,1,1,0.5,1,1,0.5,1,1,0,0,1]
recall
0.74375 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,1,0.5,1,1,0,0.5,0,1,1,1,1,0.5,0,1,0.5,0.5,1,0.5,0.5,1,1,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,0.5,1,1,1,0,1,1,1,1,0,0,1]
recall
0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,0,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0,1,1,1,1,0.5,1,0,0.5,0.5,1,0.5,0,0,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1]
recall
0.71875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,0.5,0,1,0.5,0,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,0.5,1,1,0.5,1,1,0.5,0,1,1,0,1,1,0,1,0.5,0,0.5,1,1,0.5,1,0.5,0,1,1,1,1,0.5,0,0,0.5,0,0.5,1,0,1,1,1,1,1,0,1,0.5,0,0,1,0.5]
recall
0.73125 [0,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0,1,0.5,0.5,0.5,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,0,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0,1,0.5,0,1,0,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1]
recall
0.71875 [0,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,0,1,1,1,1,1,0,0,0.5,1,0,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0.5,1,0.5,0,1,0.5,1,1,0,0.5,1,1,0,0,0,1,1,0,1,1,1,0.5,0,0,0,0,1,0,1,0,0.5,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1]
relative_absolute_error
0.7433559222918305
relative_absolute_error
0.7388988147380776
relative_absolute_error
0.7350448354535942
relative_absolute_error
0.7373422002712391
relative_absolute_error
0.7317239712732817
relative_absolute_error
0.7378591577351525
relative_absolute_error
0.7241358069332794
relative_absolute_error
0.739336523444357
relative_absolute_error
0.7400657174807453
relative_absolute_error
0.7406160075891589
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
0.07879941032168541
root_mean_squared_error
0.07821339672670702
root_mean_squared_error
0.07760250650270503
root_mean_squared_error
0.07797824768287119
root_mean_squared_error
0.07775391130970724
root_mean_squared_error
0.07819838033907739
root_mean_squared_error
0.07663394751605682
root_mean_squared_error
0.07823440055021731
root_mean_squared_error
0.07818402782025576
root_mean_squared_error
0.07856317554884877
root_relative_squared_error
0.7919638719342086
root_relative_squared_error
0.7860742137021158
root_relative_squared_error
0.7799345359411741
root_relative_squared_error
0.7837108768893453
root_relative_squared_error
0.7814562115056666
root_relative_squared_error
0.7859232933279522
root_relative_squared_error
0.7702001518622681
root_relative_squared_error
0.7862853100710445
root_relative_squared_error
0.7857790450863525
root_relative_squared_error
0.7895896231344088
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
3288.3053689993176
usercpu_time_millis
3226.9820939991405
usercpu_time_millis
3282.4867310000627
usercpu_time_millis
3230.602139000439
usercpu_time_millis
3281.181889000436
usercpu_time_millis
3272.9788030001146
usercpu_time_millis
3192.8698789997725
usercpu_time_millis
3203.591401000267
usercpu_time_millis
3216.980889000297
usercpu_time_millis
3242.5182010001663
usercpu_time_millis_testing
39.833365000049525
usercpu_time_millis_testing
39.62104599941085
usercpu_time_millis_testing
40.35154700068233
usercpu_time_millis_testing
39.20623100020748
usercpu_time_millis_testing
38.95539800032566
usercpu_time_millis_testing
39.449784000680665
usercpu_time_millis_testing
39.404541999829235
usercpu_time_millis_testing
39.11822200007009
usercpu_time_millis_testing
39.25799500029825
usercpu_time_millis_testing
39.14837299998908
usercpu_time_millis_training
3248.472003999268
usercpu_time_millis_training
3187.3610479997296
usercpu_time_millis_training
3242.1351839993804
usercpu_time_millis_training
3191.3959080002314
usercpu_time_millis_training
3242.2264910001104
usercpu_time_millis_training
3233.529018999434
usercpu_time_millis_training
3153.4653369999432
usercpu_time_millis_training
3164.473179000197
usercpu_time_millis_training
3177.7228939999986
usercpu_time_millis_training
3203.369828000177