10093027
1
Jan van Rijn
9954
Supervised Classification
8815
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,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)
8018138
axis
0
8778
copy
true
8778
missing_values
"NaN"
8778
strategy
"most_frequent"
8778
verbose
0
8778
copy
true
8779
with_mean
true
8779
with_std
true
8779
memory
null
8780
copy
true
8781
fill_value
-1
8781
missing_values
NaN
8781
strategy
"constant"
8781
verbose
0
8781
categorical_features
null
8782
categories
null
8782
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
8782
handle_unknown
"ignore"
8782
n_values
null
8782
sparse
true
8782
class_weight
null
8783
criterion
"entropy"
8783
max_depth
null
8783
max_features
1.0
8783
max_leaf_nodes
null
8783
min_impurity_decrease
0.0
8783
min_impurity_split
null
8783
min_samples_leaf
11
8783
min_samples_split
19
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
28112
8783
splitter
"best"
8783
n_jobs
null
8812
remainder
"passthrough"
8812
sparse_threshold
0.3
8812
transformer_weights
null
8812
memory
null
8813
memory
null
8815
threshold
0.0
8816
openml-python
Sklearn_0.20.0.
1491
one-hundred-plants-margin
https://www.openml.org/data/download/1592283/phpCsX3fx
-1
21106591
description
https://api.openml.org/data/download/21106591/description.xml
-1
21106592
predictions
https://api.openml.org/data/download/21106592/predictions.arff
area_under_roc_curve
0.8399479166666665 [0.829072,0.869792,0.904928,0.928267,0.901377,0.861328,0.801018,0.897293,0.867089,0.837851,0.931897,0.898497,0.833491,0.762251,0.837358,0.822838,0.905086,0.791075,0.794448,0.727884,0.898122,0.833866,0.833787,0.999901,0.801215,0.754814,0.82631,0.835148,0.835208,0.872317,0.987966,0.935705,0.834793,0.818478,0.900331,0.870561,0.794725,0.760614,0.965633,0.96514,0.735322,0.861683,0.871015,0.862926,0.867878,0.764481,0.935507,0.806483,0.727865,0.834635,0.95492,0.824732,0.929194,0.74785,0.76959,0.687441,0.998777,0.929885,0.82852,0.790443,0.774996,0.766927,0.926353,0.640428,0.835977,0.862216,0.788569,0.933495,0.662701,0.724925,0.749388,0.933318,0.80236,0.864623,0.649089,0.931305,0.900844,0.719993,0.901653,0.93026,0.928859,0.767598,0.766907,0.796914,0.857067,0.836194,0.857382,0.854344,0.735006,0.834537,0.96583,0.96437,0.824298,0.786794,0.86482,0.865136,0.861012,0.799025,0.679214,0.793442]
average_cost
0
f_measure
0.4064506658766277 [0.333333,0.628571,0.785714,0.296296,0.516129,0.275862,0.3125,0.571429,0.258065,0.428571,0.514286,0.540541,0.457143,0.066667,0.611111,0.214286,0.702703,0.186047,0.4,0.230769,0.5,0.516129,0.35,0.941176,0.424242,0.2,0.258065,0.5,0.592593,0.666667,0.375,0.8,0.470588,0.210526,0.571429,0.727273,0.296296,0.216216,0.714286,0.648649,0.285714,0.333333,0.666667,0.484848,0.645161,0.142857,0.705882,0.484848,0.206897,0.47619,0.465116,0.25,0.424242,0.16,0.333333,0,0.888889,0.5,0.1875,0.26087,0.4,0.342857,0.461538,0.258065,0.466667,0.324324,0.068966,0.5625,0.216216,0.111111,0.064516,0.685714,0.378378,0.451613,0.083333,0.571429,0.592593,0.076923,0.740741,0.580645,0.645161,0.32,0.4,0.133333,0.341463,0.5625,0.24,0.086957,0.2,0.388889,0.702703,0.611111,0.444444,0.173913,0.375,0.428571,0.424242,0.16,0,0.357143]
kappa
0.4116161616161616
kb_relative_information_score
868.8852424268293
mean_absolute_error
0.013352925465487305
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
0.41264246344483246 [0.357143,0.578947,0.916667,0.363636,0.533333,0.307692,0.3125,0.526316,0.266667,0.5,0.473684,0.47619,0.421053,0.071429,0.55,0.25,0.619048,0.148148,0.428571,0.3,0.45,0.533333,0.291667,0.888889,0.411765,0.214286,0.266667,0.5,0.727273,0.647059,0.375,0.736842,0.444444,0.181818,0.526316,0.705882,0.363636,0.190476,0.833333,0.571429,0.333333,0.269231,0.714286,0.470588,0.666667,0.166667,0.666667,0.470588,0.230769,0.384615,0.37037,0.208333,0.411765,0.222222,0.357143,0,0.8,0.416667,0.1875,0.428571,0.428571,0.315789,0.391304,0.266667,0.5,0.285714,0.076923,0.5625,0.190476,0.1,0.066667,0.631579,0.333333,0.466667,0.125,0.526316,0.727273,0.1,0.909091,0.6,0.666667,0.444444,0.428571,0.142857,0.28,0.5625,0.333333,0.142857,0.5,0.35,0.619048,0.55,0.545455,0.285714,0.375,0.346154,0.411765,0.222222,0,0.416667]
predictive_accuracy
0.4175
prior_entropy
6.6438561897747395
recall
0.4175 [0.3125,0.6875,0.6875,0.25,0.5,0.25,0.3125,0.625,0.25,0.375,0.5625,0.625,0.5,0.0625,0.6875,0.1875,0.8125,0.25,0.375,0.1875,0.5625,0.5,0.4375,1,0.4375,0.1875,0.25,0.5,0.5,0.6875,0.375,0.875,0.5,0.25,0.625,0.75,0.25,0.25,0.625,0.75,0.25,0.4375,0.625,0.5,0.625,0.125,0.75,0.5,0.1875,0.625,0.625,0.3125,0.4375,0.125,0.3125,0,1,0.625,0.1875,0.1875,0.375,0.375,0.5625,0.25,0.4375,0.375,0.0625,0.5625,0.25,0.125,0.0625,0.75,0.4375,0.4375,0.0625,0.625,0.5,0.0625,0.625,0.5625,0.625,0.25,0.375,0.125,0.4375,0.5625,0.1875,0.0625,0.125,0.4375,0.8125,0.6875,0.375,0.125,0.375,0.5625,0.4375,0.125,0,0.3125]
relative_absolute_error
0.6743901750246102
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08918241922496314
root_relative_squared_error
0.8963170377738803
total_cost
0
area_under_roc_curve
0.844221573720245 [0.990566,0.995253,1,0.987421,0.993671,0.993711,0.743671,0.996855,0.996835,1,0.996835,0.738924,0.732595,0.724684,1,0.990566,0.996835,0.484177,0.487342,0.477987,0.996855,0.481132,0.987421,1,0.984277,0.738924,0.742089,0.990506,0.742089,0.5,0.990506,1,0.745253,0.734177,1,0.487421,0.974843,0.484277,1,1,1,0.984277,0.75,0.993671,0.996835,0.990506,1,1,0.468354,0.737342,0.738924,0.737342,0.981132,0.721519,0.993711,0.987421,0.996835,1,0.968553,0.731013,0.490566,0.996855,0.737342,0.477848,1,0.993671,0.981013,1,0.455975,0.949686,0.672468,0.996855,0.743671,0.737342,0.990566,0.987342,0.996835,0.982595,0.746835,0.995253,1,0.477848,0.987421,0.996855,0.732595,0.738924,0.958861,0.72943,1,0.474843,0.987342,1,0.996835,0.963608,0.992089,0.735759,0.993711,0.493671,0.452532,0.996855]
area_under_roc_curve
0.8536967100549315 [1,1,0.748418,1,0.990506,1,0.735759,0.996855,0.743671,1,0.995253,0.743671,0.743671,0.742089,0.996835,0.996855,1,0.723101,0.735759,0.993711,1,0.971698,0.984277,1,0.996855,0.713608,0.976266,0.493671,0.993671,1,0.998418,1,0.984177,0.734177,0.974684,1,0.471698,0.990566,0.987421,0.996835,0.490566,0.996855,1,0.995253,0.742089,0.731013,0.993711,0.993711,0.471519,0.990506,0.911392,0.726266,0.996855,0.968354,1,0.471698,1,0.996855,0.990566,0.716772,1,0.993711,0.996835,0.496835,1,0.993671,0.735759,0.981132,0.455975,0.955975,0.949367,1,0.742089,0.993671,0.946541,0.996835,0.977848,0.723101,0.998418,0.75,0.985759,0.735759,0.459119,0.455975,0.974684,0.704114,0.487342,0.950949,0.740506,0.490566,0.746835,0.996855,0.957278,0.718354,0.973101,0.987342,0.996855,0.732595,0.455696,0.493711]
area_under_roc_curve
0.8160962005413583 [0.737342,0.743671,0.976266,1,0.743671,0.96519,0.493711,1,0.990506,0.732595,0.731013,0.990506,1,0.5,0.481013,0.731013,0.996835,0.716772,0.474843,0.481013,1,0.984277,0.719937,0.996855,0.481132,0.996835,0.990506,0.996855,1,0.996835,0.968553,0.990506,0.996835,0.719937,0.487421,0.996855,0.996855,0.477987,1,1,0.490506,1,1,0.996855,0.727848,0.459119,0.746835,0.996855,0.731013,0.746835,0.993671,0.996855,0.977987,0.474684,0.996855,0.974684,1,0.988924,0.987421,0.471519,0.496855,0.493711,0.995253,0.996855,0.982595,0.737342,0.746835,0.996835,0.481132,0.471519,0.974684,0.993671,0.484277,0.742089,0.459119,0.97943,1,0.468354,0.743671,1,0.471698,0.742089,0.996835,0.737342,0.996855,0.996855,1,0.992089,0.746835,0.996855,0.990506,0.996855,0.477987,0.737342,0.738924,0.993711,0.484177,0.985759,0.477987,0.734177]
area_under_roc_curve
0.8507365307300374 [0.738924,0.998418,1,0.996855,0.746835,0.487342,0.993711,0.97943,0.996835,0.743671,0.996835,0.992089,0.493711,0.985759,0.996835,0.988924,0.743671,0.968354,0.462264,0.455696,0.962264,1,0.746835,1,0.490566,0.985759,0.737342,0.990566,0.985759,0.746835,0.993711,0.996835,0.742089,0.974684,0.996855,0.496855,0.996855,0.924528,0.996855,0.996835,0.734177,0.977987,1,0.971698,0.996835,0.996855,0.998418,1,0.968354,0.990506,0.993671,0.987421,0.474843,0.455696,0.996855,0.96519,1,0.995253,0.993711,0.985759,0.990566,0.981132,0.971519,0.474843,0.746835,0.738924,0.731013,1,0.987421,0.732595,0.705696,0.738924,0.993711,0.97943,0.446541,0.746835,0.746835,0.474684,1,0.987342,0.487421,0.974684,1,0.696203,0.930818,0.496855,0.996855,0.993671,0.458861,0.984277,1,0.996855,0.474843,0.468354,0.998418,0.496855,0.740506,0.973101,0.946541,0.697785]
area_under_roc_curve
0.8090773276411113 [0.740506,1,0.75,1,0.490566,0.743671,0.484277,0.738924,0.487421,0.746835,0.981013,0.993671,0.996855,0.468553,0.993711,0.982595,0.75,0.996835,0.996855,0.993671,0.481013,0.718354,1,0.996835,0.742089,0.71519,0.973101,1,1,0.743671,0.993711,1,0.490506,0.46519,0.981132,1,0.724684,0.484177,0.743671,0.987342,0.992089,0.742089,0.5,0.993711,0.996855,0.484277,1,0.496835,0.746835,0.984177,0.962264,0.477987,0.998418,0.95283,0.740506,0.455696,1,0.731013,0.988924,0.484277,0.748418,0.743671,0.993711,0.493711,0.740506,0.949367,0.471698,0.987342,0.477848,0.712025,0.738924,0.996835,0.993711,0.490566,0.963608,1,0.746835,0.696203,0.992089,0.987421,0.993711,0.996835,0.477848,0.740506,0.993711,1,0.487421,1,1,0.738924,1,0.987342,0.959119,0.971698,0.748418,0.990566,0.735759,0.993711,0.459119,0.734177]
area_under_roc_curve
0.8347306394793407 [0.719937,1,1,0.984177,0.993711,0.993671,0.996855,0.742089,0.993711,0.742089,0.990506,1,0.993711,0.471698,1,0.737342,1,0.987342,0.955975,0.727848,1,0.740506,0.75,1,0.734177,0.724684,0.726266,1,1,1,0.996855,1,0.735759,0.981013,0.490566,1,0.732595,0.72943,1,0.996835,0.996835,0.732595,1,0.996855,0.996855,0.984277,1,0.746835,0.727848,0.738924,0.981132,0.968553,0.990506,0.993711,0.477848,0.471519,0.993711,0.996835,0.993671,0.993711,0.745253,0.727848,0.490566,0.993711,0.740506,0.743671,0.468553,0.996835,0.727848,1,0.468354,0.993671,0.949686,0.996855,0.455696,1,0.745253,0.71519,0.996835,0.490566,0.996855,0.490506,0.731013,0.726266,0.484277,0.487421,0.971698,0.471698,0.477848,0.990506,0.996855,0.990506,0.487421,0.981132,0.988924,0.996855,0.982595,0.484277,0.959119,0.738924]
area_under_roc_curve
0.833189286282939 [0.987342,0.484277,1,0.963608,1,0.993671,0.984177,0.996835,0.990566,0.735759,1,0.996855,0.72943,0.981132,0.490566,0.716772,0.493711,0.477987,0.712025,0.97943,0.738924,0.743671,0.738924,1,0.731013,0.471698,0.981132,0.742089,0.481132,1,0.995253,0.496855,0.996855,0.993711,1,0.75,0.990506,0.738924,0.998418,0.493711,0.484177,0.987342,0.740506,0.984177,0.996855,0.971519,0.746835,0.740506,0.971698,0.987421,1,0.987342,0.993671,0.468553,0.731013,0.721519,0.996835,0.743671,0.984177,0.915094,0.987342,0.471519,0.984277,0.481013,0.735759,0.981132,0.468553,0.998418,0.727848,0.468354,0.990566,1,0.732595,0.993711,0.446203,0.984277,1,0.965409,1,0.996855,0.995253,0.990566,0.740506,0.987342,0.993671,0.993671,0.732595,0.474843,1,0.75,0.996855,0.996835,0.731013,0.987421,0.493711,0.735759,0.984177,0.496855,0.712025,0.985759]
area_under_roc_curve
0.8172510648037579 [0.996835,0.993711,1,0.477848,1,0.716772,0.993671,0.996835,0.493711,0.996835,0.993711,0.487421,0.732595,0.974843,0.481132,0.46519,1,0.465409,0.971519,0.481013,0.977848,0.737342,0.732595,1,0.990506,0.440252,0.45283,0.738924,1,0.742089,0.981013,0.996855,1,1,0.740506,1,0.737342,0.984177,0.993671,0.996855,0.735759,0.737342,0.993671,0.490506,1,0.735759,1,1,0.962264,0.496855,0.974843,0.723101,0.737342,1,0.746835,0.723101,1,0.995253,0.481013,0.996855,0.493671,0.737342,0.984277,0.738924,0.746835,0.984277,0.993711,0.743671,0.481013,0.732595,0.465409,1,0.996835,0.468553,0.721519,1,0.974843,0.940252,1,0.962264,0.968354,1,0.732595,0.990506,0.468354,0.740506,0.990506,0.481132,1,0.737342,1,0.996835,0.734177,0.965409,0.474843,0.987342,0.984177,0.990566,0.697785,0.727848]
area_under_roc_curve
0.8679455507125233 [0.471698,0.995253,1,1,0.996835,0.990566,0.731013,0.955975,0.974684,1,0.490566,0.996855,0.996835,0.745253,0.996835,0.968553,0.993711,0.993711,1,0.949686,0.993671,1,0.987421,1,0.748418,0.981132,0.465409,0.990506,0.742089,0.996855,0.990506,1,0.949686,0.937107,0.996835,1,0.740506,0.721519,0.993671,0.996855,0.487421,0.727848,0.746835,0.735759,0.487342,0.490506,0.990566,0.97943,0.465409,0.996855,0.987342,0.982595,0.993671,0.705696,0.740506,0.471698,1,0.981132,0.742089,0.710443,0.745253,0.993671,0.988924,0.732595,0.996855,0.987421,0.97943,0.5,0.732595,0.987421,0.990566,0.984277,0.742089,0.993671,0.455696,0.732595,0.996855,0.996855,1,1,1,0.990566,0.477987,0.987421,0.976266,1,0.958861,0.977848,0.474843,0.987342,1,0.746835,0.996835,0.707278,0.993711,0.726266,0.955975,0.727848,0.731013,0.996855]
area_under_roc_curve
0.8696803349653688 [0.962264,0.484177,0.496855,0.96519,1,0.987421,0.737342,0.484277,0.735759,0.993711,1,0.993711,0.993671,0.987342,0.742089,1,1,1,0.993671,0.996855,1,0.996835,1,1,1,0.459119,0.996855,0.743671,0.484177,0.996855,0.985759,0.75,1,0.946541,0.992089,0.732595,0.726266,0.996835,1,1,0.959119,0.981013,0.998418,0.743671,1,0.738924,1,0.471519,0.993711,0.490566,0.995253,0.737342,0.996835,0.955696,0.738924,0.45283,1,0.987421,0.471519,1,0.993671,0.731013,0.993671,0.724684,0.990566,0.5,0.94462,0.993711,0.985759,0.468553,0.471698,0.490566,0.737342,0.996835,0.734177,0.996835,1,0.474843,0.477987,0.996835,1,0.493711,0.996855,0.484277,0.987342,1,0.969937,0.969937,0.455975,0.990506,1,0.996835,0.996835,0.737342,1,1,1,0.996835,0.976266,0.984277]
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
0.44418127269856306
kappa
0.43161634103019536
kappa
0.4063548450759818
kappa
0.40630797773654914
kappa
0.4125310908444866
kappa
0.4002998500749625
kappa
0.34951647917900136
kappa
0.35595895816890294
kappa
0.40015785319652725
kappa
0.5073230429118472
kb_relative_information_score
89.33733168165067
kb_relative_information_score
89.88028966400262
kb_relative_information_score
83.40694591748748
kb_relative_information_score
87.17423133328909
kb_relative_information_score
80.314682299163
kb_relative_information_score
86.09231858690346
kb_relative_information_score
83.63042271075261
kb_relative_information_score
80.48646041002516
kb_relative_information_score
91.47159388702352
kb_relative_information_score
97.09096593653983
mean_absolute_error
0.012883218010167513
mean_absolute_error
0.013040672032753105
mean_absolute_error
0.013335414966118337
mean_absolute_error
0.013493395985249703
mean_absolute_error
0.013698917174908861
mean_absolute_error
0.01345262408246638
mean_absolute_error
0.01406975499775423
mean_absolute_error
0.01378558708716894
mean_absolute_error
0.01340924810215154
mean_absolute_error
0.01236042221613526
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
0.45
predictive_accuracy
0.4375
predictive_accuracy
0.4125
predictive_accuracy
0.4125
predictive_accuracy
0.41875
predictive_accuracy
0.40625
predictive_accuracy
0.35625
predictive_accuracy
0.3625
predictive_accuracy
0.40625
predictive_accuracy
0.5125
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
0.45 [0,0.5,1,0,0.5,1,0.5,1,0,0,1,0.5,0,0,1,1,1,0,0,0,1,0,1,1,0,0.5,0.5,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,0.5,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,1,1,1,0,0.5,0.5,0,0,0,1]
recall
0.4375 [1,1,0.5,1,0,1,0.5,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,0,0.5,1,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0,1,0,0,0,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0.5,1,0.5,1,0,1,1,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0,0,0,0,0.5,1,0,0,0.5,0.5,1,0,0,0]
recall
0.4125 [0,0.5,0,1,0.5,0,0,1,0.5,0,0.5,0.5,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,0,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0,1,0.5,0,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0,1,0,0.5,0,0,1,0,0.5,1,0,0,1,0,1,0,0,0,0,1,0.5,1,0,0.5,0.5,1,0,0.5,0,0.5]
recall
0.4125 [0.5,1,1,0,0.5,0,1,0.5,0,0,1,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0,0.5,1,1,0.5,0.5,1,0,1,0,1,1,0,0,1,0,0.5,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,1,0,1,0,0.5,0.5,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0.5,1,0,0,0,1,0,0,0,1,0,0,0,0.5,0,0.5,0,0,0]
recall
0.41875 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0,0,1,0.5,0.5,0.5,1,1,0,0,1,1,0.5,0,0,1,1,0,1,1,0,0,1,1,0.5,0,0.5,1,1,0.5,0,1,1,0,1,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0,0,0,0,0,1,1,0,0,1,0.5,0,0.5,0,1,1,0,0.5,0,1,0,1,1,0.5,1,1,0,0,0.5,0,0.5,1,0,0]
recall
0.40625 [0,1,1,0,1,0,0,0.5,1,0.5,0.5,1,1,0,1,0,1,0.5,0,0,1,0.5,0,1,0.5,0,0.5,1,1,1,0,1,0,0,0,1,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,0,0,0,0,0,1,1,1,0,0.5,0,0,1,0,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0.5,0,0,0.5]
recall
0.35625 [0.5,0,1,0,1,0,0,1,0,0,1,1,0.5,0,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,0.5,0,0,0,0.5,0,1,0,0.5,0.5,0,1,1,0,0.5,0,0,0,1,0.5,0,0,0,0,1,0,0,0,0,0.5,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,1,0,0,0,0.5,1,1,0.5,0,0,0.5,0.5,0,0,0.5]
recall
0.3625 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0.5,0,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,0,0.5,1,0,1,0,1,1,0,0,0,0,0.5,1,0,0,1,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0]
recall
0.40625 [0,1,1,0,0,1,0.5,0,0,1,0,1,1,0,1,0,1,0,1,0,0.5,1,1,1,0.5,0,0,0.5,0.5,1,0,1,0,0,1,1,0.5,0,0,0,0,0.5,0,0.5,0,0,1,0.5,0,1,0.5,0.5,1,0,0,0,1,0,0,0,0,1,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,0,1,0,0.5,0,1,0.5,0,0,0,1]
recall
0.5125 [0,0,0,0,1,0,0,0,0.5,0,1,1,1,0,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,1,1,1,0,0.5,0.5,0.5,1,0,1,0,1,0,1,0,1,0,0,0,1,1,0,1,1,0,1,0,1,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0,1,1,0,1,0,0.5,1,0,0,0,0.5,1,1,1,0.5,0,1,1,0,0,0]
relative_absolute_error
0.6506675762710854
relative_absolute_error
0.6586197996339941
relative_absolute_error
0.6735058063696119
relative_absolute_error
0.6814846457196809
relative_absolute_error
0.6918645037832748
relative_absolute_error
0.6794254587104221
relative_absolute_error
0.7105936867552629
relative_absolute_error
0.6962417720792382
relative_absolute_error
0.677234752633915
relative_absolute_error
0.6242637482896586
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
0.08746847186111488
root_mean_squared_error
0.08698234656337585
root_mean_squared_error
0.09137582735737691
root_mean_squared_error
0.08918535712034124
root_mean_squared_error
0.0914318580366923
root_mean_squared_error
0.08990751006324724
root_mean_squared_error
0.09217295604963562
root_mean_squared_error
0.09110798422146887
root_mean_squared_error
0.08834876857842745
root_mean_squared_error
0.0834843608101919
root_relative_squared_error
0.8790912186335672
root_relative_squared_error
0.8742054755617484
root_relative_squared_error
0.9183616189476098
root_relative_squared_error
0.8963465647334032
root_relative_squared_error
0.9189247484628761
root_relative_squared_error
0.9036044748936156
root_relative_squared_error
0.926373063741091
root_relative_squared_error
0.9156696941461578
root_relative_squared_error
0.8879385335290441
root_relative_squared_error
0.8390493959698699
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
222.27354499955254
usercpu_time_millis
204.006751999259
usercpu_time_millis
181.71193699981814
usercpu_time_millis
146.11291499932122
usercpu_time_millis
146.61534999959258
usercpu_time_millis
145.03117999993265
usercpu_time_millis
146.48745500016958
usercpu_time_millis
147.07077400089474
usercpu_time_millis
147.51718099887512
usercpu_time_millis
151.6315979997671
usercpu_time_millis_testing
1.731983999889053
usercpu_time_millis_testing
1.7128639992733952
usercpu_time_millis_testing
1.241897999534558
usercpu_time_millis_testing
1.2415089995556627
usercpu_time_millis_testing
1.2369659998512361
usercpu_time_millis_testing
1.2591650001922972
usercpu_time_millis_testing
1.2384450001263758
usercpu_time_millis_testing
1.2555700004668324
usercpu_time_millis_testing
1.4077159994485555
usercpu_time_millis_testing
1.2398150001899921
usercpu_time_millis_training
220.5415609996635
usercpu_time_millis_training
202.2938879999856
usercpu_time_millis_training
180.47003900028358
usercpu_time_millis_training
144.87140599976556
usercpu_time_millis_training
145.37838399974135
usercpu_time_millis_training
143.77201499974035
usercpu_time_millis_training
145.2490100000432
usercpu_time_millis_training
145.8152040004279
usercpu_time_millis_training
146.10946499942656
usercpu_time_millis_training
150.39178299957712