10012865
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)
7937586
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
"median"
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
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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
false
8919
class_weight
null
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criterion
"gini"
8919
max_depth
null
8919
max_features
0.6859992664120714
8919
max_leaf_nodes
null
8919
min_impurity_decrease
0.0
8919
min_impurity_split
null
8919
min_samples_leaf
9
8919
min_samples_split
6
8919
min_weight_fraction_leaf
0.0
8919
n_estimators
100
8919
n_jobs
null
8919
oob_score
false
8919
random_state
12504
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
20946253
description
https://api.openml.org/data/download/20946253/description.xml
-1
20946254
predictions
https://api.openml.org/data/download/20946254/predictions.arff
area_under_roc_curve
0.9891915246212121 [0.980074,0.996883,0.99854,0.999171,0.999527,0.994437,0.996883,0.996765,0.99637,0.999803,0.992069,0.994871,0.997159,0.974195,0.998895,0.992779,0.998461,0.977352,0.983467,0.977233,0.998185,0.998895,0.995305,0.999763,0.997435,0.952257,0.952671,0.994121,0.974096,0.999487,0.997041,0.959221,0.995462,0.980666,0.996528,0.994318,0.990807,0.973603,0.996054,0.998777,0.99858,0.998737,0.995857,0.989583,0.995699,0.996765,0.992937,0.998224,0.99349,0.989741,0.995778,0.984178,0.994081,0.97459,0.982481,0.971985,0.999388,0.993213,0.988163,0.966698,0.999763,0.996686,0.955196,0.990767,0.998264,0.990096,0.979956,0.995778,0.945707,0.981613,0.978693,0.996409,0.987098,0.994239,0.964725,0.996488,0.998461,0.977588,0.99783,0.995818,0.990688,0.991162,0.996488,0.98984,0.991832,0.984888,0.997001,0.97893,0.99637,0.999014,0.998185,0.998185,0.994594,0.986387,0.988479,0.992148,0.99416,0.985598,0.955926,0.998303]
average_cost
0
f_measure
0.6209452522827066 [0.486486,0.594595,0.848485,0.785714,0.8,0.685714,0.555556,0.645161,0.666667,0.9375,0.551724,0.764706,0.702703,0.571429,0.769231,0.666667,0.857143,0.387097,0.384615,0.137931,0.666667,0.758621,0.578947,0.888889,0.75,0,0.076923,0.580645,0.742857,0.810811,0.8,0.742857,0.787879,0.30303,0.611111,0.6,0.432432,0.285714,0.571429,0.857143,0.777778,0.848485,0.722222,0.647059,0.615385,0.705882,0.666667,0.75,0.685714,0.727273,0.594595,0.514286,0.5625,0.416667,0.666667,0.30303,0.969697,0.727273,0.342857,0.32,0.882353,0.75,0.529412,0.628571,0.742857,0.555556,0.285714,0.551724,0.272727,0.48,0.315789,0.689655,0.6,0.827586,0.413793,0.774194,0.8,0.615385,0.848485,0.714286,0.580645,0.645161,0.514286,0.4,0.645161,0.740741,0.857143,0.580645,0.722222,0.823529,0.777778,0.8,0.689655,0.461538,0.740741,0.628571,0.413793,0.5,0.285714,0.8]
kappa
0.6281565656565656
kb_relative_information_score
1130.4166676896834
mean_absolute_error
0.012659360219008937
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
0.6357760278189676 [0.428571,0.52381,0.823529,0.916667,0.736842,0.631579,0.5,0.666667,0.714286,0.9375,0.615385,0.722222,0.619048,0.666667,0.652174,0.714286,0.789474,0.4,0.5,0.153846,0.565217,0.846154,0.5,0.8,0.75,0,0.1,0.6,0.684211,0.714286,0.736842,0.684211,0.764706,0.294118,0.55,0.5,0.380952,0.263158,0.666667,1,0.7,0.823529,0.65,0.611111,0.8,0.666667,0.647059,0.75,0.631579,0.705882,0.52381,0.473684,0.5625,0.625,0.647059,0.294118,0.941176,0.705882,0.315789,0.444444,0.833333,0.625,0.5,0.578947,0.684211,0.5,0.263158,0.615385,0.5,0.666667,1,0.769231,0.642857,0.923077,0.461538,0.8,0.736842,0.8,0.823529,0.833333,0.6,0.666667,0.473684,0.368421,0.666667,0.909091,1,0.6,0.65,0.777778,0.7,0.736842,0.769231,0.6,0.909091,0.578947,0.461538,0.583333,0.6,0.736842]
predictive_accuracy
0.631875
prior_entropy
6.6438561897747395
recall
0.631875 [0.5625,0.6875,0.875,0.6875,0.875,0.75,0.625,0.625,0.625,0.9375,0.5,0.8125,0.8125,0.5,0.9375,0.625,0.9375,0.375,0.3125,0.125,0.8125,0.6875,0.6875,1,0.75,0,0.0625,0.5625,0.8125,0.9375,0.875,0.8125,0.8125,0.3125,0.6875,0.75,0.5,0.3125,0.5,0.75,0.875,0.875,0.8125,0.6875,0.5,0.75,0.6875,0.75,0.75,0.75,0.6875,0.5625,0.5625,0.3125,0.6875,0.3125,1,0.75,0.375,0.25,0.9375,0.9375,0.5625,0.6875,0.8125,0.625,0.3125,0.5,0.1875,0.375,0.1875,0.625,0.5625,0.75,0.375,0.75,0.875,0.5,0.875,0.625,0.5625,0.625,0.5625,0.4375,0.625,0.625,0.75,0.5625,0.8125,0.875,0.875,0.875,0.625,0.375,0.625,0.6875,0.375,0.4375,0.1875,0.875]
relative_absolute_error
0.6393616272226724
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.07403976778752809
root_relative_squared_error
0.7441276645947655
total_cost
0
area_under_roc_curve
0.9872691266618899 [1,0.987342,1,1,0.993671,1,0.996835,1,0.974684,1,0.984177,0.993671,0.993671,0.962025,1,0.974843,0.993671,0.962025,0.974684,0.974843,0.987421,1,0.987421,1,1,0.996835,0.844937,1,0.977848,1,0.968354,0.996835,0.974684,0.958861,1,0.987421,1,0.993711,1,1,1,1,1,0.993671,1,1,1,1,1,0.955696,0.993671,1,0.987421,0.984177,1,1,1,1,0.987421,0.996835,1,1,1,0.981013,1,0.993671,0.990506,1,1,0.993711,0.962025,1,0.968354,1,0.993711,1,1,0.984177,1,1,1,1,0.993711,1,0.984177,0.952532,0.987342,1,0.987342,0.974843,1,1,1,0.996835,1,0.981013,1,0.996835,0.838608,1]
area_under_roc_curve
0.9851111575511504 [0.993711,1,1,1,1,0.987421,1,1,1,1,0.996835,0.993671,1,0.977848,1,1,0.993671,0.936709,0.958861,0.974843,1,1,0.993711,1,1,0.911392,0.987342,0.949367,1,1,1,1,1,0.990506,1,0.993711,0.981132,1,0.993711,1,1,1,1,0.927215,1,1,1,1,0.993671,1,1,0.946203,1,1,0.987421,0.886792,1,1,0.987421,0.822785,1,0.974843,1,0.96519,1,0.996835,0.987342,1,0.685535,0.974843,0.968354,1,1,1,1,1,0.987342,0.933544,1,0.990506,0.96519,0.990506,0.981132,0.993711,0.987342,1,1,1,0.996835,1,0.993671,1,1,0.993671,1,1,1,0.971519,1,1]
area_under_roc_curve
0.9929275137329828 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.993671,1,1,1,0.990506,0.974843,0.927215,1,1,0.993671,1,1,0.987342,0.996835,0.993711,1,1,0.993711,1,1,0.971519,1,1,0.993711,0.930818,1,1,0.996835,1,1,1,0.996835,1,0.971519,1,0.993671,1,0.993671,1,1,1,1,0.981013,1,1,0.981132,0.943038,1,0.993711,0.996835,1,0.987342,0.996835,0.993671,0.996835,0.974843,0.981013,0.962025,1,1,0.987342,1,1,1,0.968354,0.993671,0.996835,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,0.990506,1,1,0.993671,0.981013,0.955975,1]
area_under_roc_curve
0.9935201218055884 [0.990506,1,1,1,1,0.981013,1,1,0.990506,1,0.993671,1,1,0.990506,1,1,1,1,0.949686,0.962025,1,1,1,1,0.993711,0.987342,0.927215,1,0.996835,1,1,0.993671,1,0.987342,0.993711,0.993711,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,0.993671,0.981013,1,1,0.981132,0.917722,1,0.990506,1,0.993671,0.993711,1,1,0.993711,0.993671,1,0.993671,0.996835,0.981013,0.996835,1,0.993671,0.981013,0.996835,0.987421,1,0.993711,0.996835,1,1,1,0.996835,0.987421,0.993671,1,0.993671,1,0.962264,1,1,1,1,1,1,0.993711,1,1,1,0.996835,0.996835,1,0.990506]
area_under_roc_curve
0.9833077083830902 [0.971519,0.993711,1,1,1,1,1,0.996835,1,1,0.990506,0.990506,1,0.855346,1,1,1,0.996835,1,0.990506,0.990506,1,1,1,1,0.971519,0.952532,1,1,1,1,0.367925,1,0.946203,1,1,0.990506,0.911392,0.993671,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.968553,0.996835,0.993711,0.920886,0.962025,0.996855,0.993671,0.996835,1,1,1,0.987421,1,1,1,0.949686,0.990506,0.933544,0.96519,0.996835,1,1,0.962264,0.863924,1,1,0.977848,1,1,1,1,0.993671,0.990506,0.981132,0.974843,1,1,1,0.996835,1,0.993671,0.987421,1,0.924051,0.993711,0.993671,1,0.968553,0.996835]
area_under_roc_curve
0.993795279038293 [0.977848,1,1,1,1,1,1,0.993671,1,0.996835,0.990506,1,1,1,1,1,1,0.977848,0.993711,0.96519,1,1,0.993671,1,0.996835,0.996835,0.981013,1,1,1,1,1,0.984177,0.996835,0.993711,1,0.987342,0.990506,1,1,1,0.996835,0.993711,0.993711,0.993711,1,1,0.996835,0.996835,0.990506,1,1,0.990506,1,0.996835,0.984177,1,1,0.993671,1,1,1,0.987421,1,1,0.990506,0.987421,1,0.958861,0.993671,0.981013,0.990506,1,1,0.946203,1,0.996835,0.993671,1,1,1,0.984177,1,0.996835,0.981132,1,1,0.993711,0.981013,1,1,0.993671,0.962264,1,0.996835,1,1,0.981132,0.993711,0.996835]
area_under_roc_curve
0.9943620133747311 [1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.949367,1,1,0.987342,0.990506,0.993671,0.996835,0.990506,1,0.993671,0.90566,0.987421,0.993671,1,1,1,0.993711,1,0.955975,1,1,1,1,1,1,0.993671,1,1,1,1,0.990506,0.990506,0.996835,1,1,0.993711,1,1,0.993711,0.993671,1,1,0.996835,0.990506,0.993711,1,1,1,0.990506,1,1,0.949686,0.996835,0.993671,0.993671,0.993711,1,1,1,0.996835,1,1,1,0.993711,1,1,1,0.996835,0.993671,1,1,1,0.842767,1,1,1,1,0.993671,0.930818,0.993711,0.996835,1,1,0.993671,1]
area_under_roc_curve
0.9889743053896979 [0.993671,1,1,0.996835,1,0.993671,1,1,1,1,0.987421,1,1,1,0.987421,0.996835,1,0.974843,0.968354,0.993671,1,1,1,1,1,0.981132,0.993711,1,0.720126,1,1,1,1,1,1,1,0.981013,0.984177,1,1,1,1,1,0.996835,0.993711,0.984177,1,1,0.987421,1,0.987421,0.981013,0.984177,1,0.996835,0.924051,1,1,0.981013,0.974843,1,0.993671,0.993711,0.996835,1,0.899371,0.993711,0.993671,0.936709,0.974684,0.968553,1,0.996835,1,0.993671,1,1,1,1,1,0.987342,0.968553,1,0.987342,0.987342,0.962025,1,0.949686,1,1,0.993711,1,1,0.937107,1,0.977848,0.996835,0.937107,0.962025,1]
area_under_roc_curve
0.9915514787835361 [0.987421,0.993671,1,1,1,0.993711,0.987342,0.993711,1,1,0.981132,1,1,1,1,1,1,1,1,0.993711,1,0.996835,0.968553,1,1,0.861635,0.962264,1,1,1,1,1,1,1,0.996835,1,1,0.955696,1,0.993711,1,0.990506,0.996835,0.996835,0.993671,1,1,1,0.987421,1,1,0.984177,0.996835,0.946203,0.977848,0.962264,1,0.993711,1,0.993671,1,1,0.996835,0.987342,1,1,0.977848,1,0.955696,1,1,0.981132,0.987342,1,0.984177,0.987342,1,0.987421,1,0.993671,1,1,1,0.987421,1,1,1,0.943038,0.981132,1,1,1,1,0.96519,0.968553,0.993671,0.993711,0.993671,0.962025,1]
area_under_roc_curve
0.9892238376721597 [0.855346,1,0.968553,1,1,0.993711,1,0.981132,0.996835,1,1,1,0.984177,0.949367,1,0.981132,1,0.981132,0.993671,1,1,1,1,1,1,0.886792,0.993711,0.987342,0.993671,1,1,1,1,0.987421,0.990506,0.993671,0.993671,0.990506,0.993671,1,1,1,1,1,1,1,1,0.996835,1,0.993711,0.996835,0.996835,1,0.977848,1,0.974843,1,1,0.993671,0.990506,1,1,0.727848,0.996835,1,1,0.977848,0.993711,0.981013,0.949686,0.955975,1,0.971519,1,1,0.996835,1,1,1,0.993671,0.996835,0.981132,1,0.968553,0.996835,0.993671,1,0.987342,1,0.996835,1,1,0.993671,1,1,1,0.974843,1,0.996835,1]
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
0.5389958951689295
kappa
0.6337083086639036
kappa
0.6463669732012473
kappa
0.6715746259819209
kappa
0.6462413139608338
kappa
0.6336938501618378
kappa
0.6651397883430737
kappa
0.6398530979741737
kappa
0.6273046705357495
kappa
0.5768198326227697
kb_relative_information_score
111.01513459820862
kb_relative_information_score
111.7130366903828
kb_relative_information_score
113.61125147465789
kb_relative_information_score
115.02774303904384
kb_relative_information_score
111.5172232194959
kb_relative_information_score
113.90773938088361
kb_relative_information_score
117.31896424477956
kb_relative_information_score
112.73595512876949
kb_relative_information_score
112.47624730195628
kb_relative_information_score
111.09337261150773
mean_absolute_error
0.01259266414569907
mean_absolute_error
0.012683801980290954
mean_absolute_error
0.0126441373232568
mean_absolute_error
0.012695390219869993
mean_absolute_error
0.012629846268417039
mean_absolute_error
0.01274915900816912
mean_absolute_error
0.012264311404077948
mean_absolute_error
0.01258168521052069
mean_absolute_error
0.012781169567156695
mean_absolute_error
0.012971437062630995
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
0.54375
predictive_accuracy
0.6375
predictive_accuracy
0.65
predictive_accuracy
0.675
predictive_accuracy
0.65
predictive_accuracy
0.6375
predictive_accuracy
0.66875
predictive_accuracy
0.64375
predictive_accuracy
0.63125
predictive_accuracy
0.58125
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
0.54375 [1,0.5,1,1,0,1,0.5,1,0,1,0,0.5,0.5,0,1,0,1,0,0.5,0,0,0,0,1,0,0,0,1,0.5,1,0.5,1,0.5,0,1,0,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0,0,1,1,0.5,0.5,1,0.5,0.5,1,0,0,0,0,0,1,0,1,0.5,0.5,1,1,0,1,1,1,0.5,0.5,0,1,0.5,0,1,1,0.5,0.5,0.5,0.5,1,0,0,1]
recall
0.6375 [1,1,1,1,1,0,0.5,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0.5,0,1,1,1,0,1,1,0,0,0,1,1,1,1,0.5,0.5,0,0,0,1,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,0.5,1,0,1,1,0,0,1,1,1,0.5,1,0,0,1,0,0,0,1,1,1,0,1,0.5,0.5,1,0,0.5,0.5,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,0]
recall
0.65 [1,0.5,1,0,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,0,0,0,1,1,0.5,1,1,0,0,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,0.5,1,0.5,1,0.5,1,1,0,1,0,1,1,0,0.5,1,1,0.5,1,0.5,1,0.5,0.5,0,1,0,1,1,0.5,0,1,1,0.5,0,1,0,0.5,0.5,0,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,0,0.5]
recall
0.675 [0.5,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0,0,0,0,1,1,1,0.5,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0,1,1,0,0,1,0.5,1,0.5,1,0.5,1,0,0.5,1,0.5,1,0.5,0.5,0,0,0.5,0.5,1,1,1,0.5,1,0.5,1,0.5,1,0.5,1,0.5,1,0,1,0,1,1,1,1,0,0.5,1,1,0,1,0,1]
recall
0.65 [0,0,1,0.5,1,1,0,0.5,1,1,0.5,0.5,0,0,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,0.5,1,0,1,0,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0,0.5,0,1,1,1,0,1,1,1,1,0.5,1,0,0,0,0.5,0.5,0.5,1,0,0.5,1,1,0.5,1,1,1,1,0,0.5,0,0,1,1,1,1,1,0,0,1,0.5,1,0.5,1,0,1]
recall
0.6375 [0,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,1,1,0.5,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0.5,0.5,0,0.5,0.5,0.5,0.5,0,1,1,0,1,1,1,0.5,0,1,0.5,1,0,0.5,1,0.5,0.5,1,1,0,1,1,1,0,1,1,0,1,1,0.5,0,0,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,0,1,1,1,0,1,0.5,1,1,0,0,1]
recall
0.66875 [1,0,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,0.5,0.5,1,0,0,0,0.5,1,1,0.5,0,1,0,1,1,0.5,1,1,1,0.5,1,1,0.5,0,0.5,0,0.5,1,1,0,1,1,0,0.5,0.5,1,0.5,0,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,1,1,0,0,0,0,1,1,0,1]
recall
0.64375 [1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,0,0.5,1,0,0.5,0.5,0.5,0.5,1,1,1,0,0,0.5,0,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,0.5,1,0.5,0.5,1,1,0,0,1,1,1,1,1,0,1,0,0,0.5,0,1,0.5,1,0.5,1,1,1,1,1,0,0,0,0,0,0.5,1,0,1,1,0,1,1,0,1,0.5,0,0,0,1]
recall
0.63125 [0,1,1,1,1,1,0,1,0.5,1,0,1,1,0.5,1,0,1,1,1,0,1,0.5,0,1,1,0,0,1,1,1,1,1,1,1,0.5,1,0,0,0.5,0,1,0.5,0.5,0.5,0,0.5,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,0.5,0.5,0.5,1,0.5,0.5,1,1,0,1,0.5,1,0,0,0,0.5,0,0.5,1,0,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0,0,0.5,0,0.5,0.5,1]
recall
0.58125 [0,0.5,0,0.5,1,1,1,0,1,1,1,1,0.5,0.5,1,0,1,0,0,0,1,0.5,1,1,1,0,0,0.5,0.5,1,1,1,1,0,0.5,0.5,0.5,0,0,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0.5,1,0,0.5,1,0,1,0,1,0,1,1,0,0.5,1,1,0,0,0,0,0,1,0,0.5,0,1,1,0,1,0,1,0,1,0,1,0.5,0,0.5,1,0.5,1,1,1,0,0,1,0,0.5,0.5,1]
relative_absolute_error
0.6359931386716692
relative_absolute_error
0.6405960596106531
relative_absolute_error
0.6385927941038776
relative_absolute_error
0.6411813242358572
relative_absolute_error
0.6378710236574251
relative_absolute_error
0.6438969196044999
relative_absolute_error
0.6194096668726226
relative_absolute_error
0.6354386469959934
relative_absolute_error
0.6455136145028624
relative_absolute_error
0.6551230839712613
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
0.07494129447472285
root_mean_squared_error
0.07374265712618128
root_mean_squared_error
0.07329513022420653
root_mean_squared_error
0.07392022742253912
root_mean_squared_error
0.07403674704233813
root_mean_squared_error
0.07371306952124551
root_mean_squared_error
0.07191248675350417
root_mean_squared_error
0.07358500869144913
root_mean_squared_error
0.07489780118024496
root_mean_squared_error
0.07627202959320385
root_relative_squared_error
0.7531883487157266
root_relative_squared_error
0.7411415900950635
root_relative_squared_error
0.7366437754967592
root_relative_squared_error
0.7429262387221277
root_relative_squared_error
0.744097304963304
root_relative_squared_error
0.7408442234768295
root_relative_squared_error
0.7227476857659881
root_relative_squared_error
0.7395571637108412
root_relative_squared_error
0.7527512246591218
root_relative_squared_error
0.7665627398773952
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
3534.766575998219
usercpu_time_millis
3467.673150000337
usercpu_time_millis
3700.740494999991
usercpu_time_millis
3527.171427000212
usercpu_time_millis
3644.5042499999545
usercpu_time_millis
3563.5151459991903
usercpu_time_millis
3552.3842929997045
usercpu_time_millis
3541.2195960016106
usercpu_time_millis
3512.4516359992413
usercpu_time_millis
3544.4597250007064
usercpu_time_millis_testing
40.266738998980145
usercpu_time_millis_testing
40.90976299994509
usercpu_time_millis_testing
40.57716799979971
usercpu_time_millis_testing
40.53089800072485
usercpu_time_millis_testing
40.41951299950597
usercpu_time_millis_testing
41.11317100068845
usercpu_time_millis_testing
40.4178190001403
usercpu_time_millis_testing
40.61031700075546
usercpu_time_millis_testing
41.47617799935688
usercpu_time_millis_testing
40.76773300039349
usercpu_time_millis_training
3494.499836999239
usercpu_time_millis_training
3426.763387000392
usercpu_time_millis_training
3660.163327000191
usercpu_time_millis_training
3486.6405289994873
usercpu_time_millis_training
3604.0847370004485
usercpu_time_millis_training
3522.401974998502
usercpu_time_millis_training
3511.966473999564
usercpu_time_millis_training
3500.609279000855
usercpu_time_millis_training
3470.9754579998844
usercpu_time_millis_training
3503.691992000313