10105594
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)
8030815
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
"gini"
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
12
8783
min_samples_split
8
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
23460
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
21131726
description
https://api.openml.org/data/download/21131726/description.xml
-1
21131727
predictions
https://api.openml.org/data/download/21131727/predictions.arff
area_under_roc_curve
0.8498674242424243 [0.858566,0.955709,0.899858,0.870068,0.834813,0.926689,0.866162,0.955216,0.804135,0.968178,0.804115,0.896484,0.803326,0.791686,0.931996,0.798256,0.966935,0.727825,0.778449,0.753018,0.957722,0.784209,0.827435,0.935093,0.89029,0.873441,0.772964,0.823587,0.899878,0.966777,0.867405,0.898615,0.894906,0.795395,0.925327,0.899384,0.826014,0.759075,0.830335,0.900075,0.830828,0.898418,0.902285,0.861841,0.794133,0.930694,0.902383,0.901061,0.8648,0.86484,0.82489,0.802616,0.860815,0.792752,0.825639,0.852766,0.968158,0.89536,0.953756,0.716501,0.898832,0.901397,0.926669,0.735105,0.931976,0.797072,0.848031,0.854976,0.713463,0.788688,0.742622,0.9317,0.830828,0.828993,0.756017,0.761324,0.964785,0.681167,0.93385,0.767479,0.822996,0.899996,0.692018,0.926551,0.793324,0.82414,0.90264,0.828973,0.895399,0.897885,0.93608,0.869318,0.821417,0.85539,0.793679,0.768663,0.825758,0.857915,0.582584,0.833097]
average_cost
0
f_measure
0.41285792962854356 [0.363636,0.408163,0.714286,0.785714,0.514286,0.4375,0.571429,0.378378,0.518519,0.9375,0.457143,0.551724,0.45,0.272727,0.571429,0.4,0.774194,0.258065,0.074074,0,0.439024,0.230769,0.292683,0.8,0.421053,0.074074,0,0.137931,0.555556,0.682927,0.428571,0.666667,0.47619,0.230769,0.470588,0.514286,0.25,0.117647,0.375,0.606061,0.482759,0.625,0.65,0.4,0,0.545455,0.666667,0.580645,0.588235,0.5,0.307692,0.424242,0.451613,0.466667,0.294118,0.214286,0.9375,0.32,0.410256,0.133333,0.516129,0.611111,0.258065,0.4,0.625,0.30303,0.176471,0.1,0.1,0.1875,0,0.606061,0.470588,0.388889,0.230769,0.37037,0.722222,0.125,0.6875,0.482759,0.105263,0.709677,0.25,0.4,0.3125,0.272727,0.727273,0.384615,0.571429,0.387097,0.903226,0.628571,0.235294,0.322581,0.222222,0.451613,0.222222,0.153846,0,0.457143]
kappa
0.4236111111111111
kb_relative_information_score
872.4725101568849
mean_absolute_error
0.01327696628399449
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
0.4170529767244468 [0.285714,0.30303,0.833333,0.916667,0.473684,0.4375,0.526316,0.333333,0.636364,0.9375,0.421053,0.615385,0.375,0.5,0.526316,0.368421,0.8,0.266667,0.090909,0,0.36,0.3,0.24,0.736842,0.363636,0.090909,0,0.153846,0.5,0.56,0.346154,0.647059,0.384615,0.3,0.444444,0.473684,0.208333,0.111111,0.375,0.588235,0.538462,0.625,0.541667,0.428571,0,0.428571,0.714286,0.6,0.555556,0.416667,0.26087,0.411765,0.466667,0.5,0.277778,0.25,0.9375,0.444444,0.347826,0.142857,0.533333,0.55,0.266667,0.333333,0.625,0.294118,0.166667,0.25,0.25,0.1875,0,0.588235,0.444444,0.35,0.3,0.454545,0.65,0.125,0.6875,0.538462,0.333333,0.733333,0.25,0.428571,0.3125,0.5,0.705882,0.5,0.526316,0.4,0.933333,0.578947,0.222222,0.333333,0.272727,0.466667,0.272727,0.2,0,0.421053]
predictive_accuracy
0.429375
prior_entropy
6.6438561897747395
recall
0.429375 [0.5,0.625,0.625,0.6875,0.5625,0.4375,0.625,0.4375,0.4375,0.9375,0.5,0.5,0.5625,0.1875,0.625,0.4375,0.75,0.25,0.0625,0,0.5625,0.1875,0.375,0.875,0.5,0.0625,0,0.125,0.625,0.875,0.5625,0.6875,0.625,0.1875,0.5,0.5625,0.3125,0.125,0.375,0.625,0.4375,0.625,0.8125,0.375,0,0.75,0.625,0.5625,0.625,0.625,0.375,0.4375,0.4375,0.4375,0.3125,0.1875,0.9375,0.25,0.5,0.125,0.5,0.6875,0.25,0.5,0.625,0.3125,0.1875,0.0625,0.0625,0.1875,0,0.625,0.5,0.4375,0.1875,0.3125,0.8125,0.125,0.6875,0.4375,0.0625,0.6875,0.25,0.375,0.3125,0.1875,0.75,0.3125,0.625,0.375,0.875,0.6875,0.25,0.3125,0.1875,0.4375,0.1875,0.125,0,0.5]
relative_absolute_error
0.6705538527269933
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08940272725861975
root_relative_squared_error
0.8985312168221832
total_cost
0
area_under_roc_curve
0.8322196431414698 [0.990566,0.985759,0.996835,1,0.493671,0.990566,0.740506,0.996855,0.487342,0.996855,0.490506,0.731013,0.742089,0.710443,1,0.481132,0.990506,0.46519,0.982595,0.981132,0.955975,0.908805,0.474843,1,0.965409,0.981013,0.72943,1,0.745253,1,0.72943,0.985759,0.737342,0.719937,0.973101,0.481132,1,0.984277,0.993711,0.745253,0.465409,0.996855,0.996835,0.731013,0.990506,0.984177,1,1,1,0.732595,0.446203,0.745253,0.984277,0.72943,0.993711,0.996855,1,1,0.971698,0.927215,0.996855,0.996855,0.985759,0.735759,0.996855,0.737342,1,0.996855,0.959119,0.990566,0.708861,1,0.484177,0.996835,0.987421,1,0.992089,0.471519,1,1,0.737342,0.993671,0.484277,1,0.481013,0.713608,0.490506,0.996835,0.987342,0.477987,1,0.996855,0.712025,0.985759,0.727848,0.484177,1,0.996835,0.468354,0.993711]
area_under_roc_curve
0.836629498049518 [0.987421,0.993671,0.998418,1,0.969937,0.95283,1,0.993711,0.998418,1,0.993671,0.738924,0.743671,0.737342,0.97943,0.990566,0.75,0.742089,0.713608,0.465409,0.996855,0.462264,0.974843,1,0.996855,0.713608,0.710443,0.481013,1,0.987421,1,1,0.988924,0.738924,0.990506,0.993711,0.493711,0.993711,0.971698,0.996835,1,0.993711,0.996835,0.746835,0.46519,1,1,1,0.992089,1,0.996835,0.742089,0.993711,0.96519,0.481132,0.465409,1,0.996855,0.996855,0.462025,1,0.487421,0.982595,0.724684,1,0.732595,0.971519,0.993711,0.455975,0.993711,0.710443,1,0.734177,0.738924,0.465409,0.734177,0.746835,0.737342,1,0.477848,0.712025,0.745253,0.977987,1,0.738924,0.727848,0.996835,0.716772,0.996835,0.987421,0.745253,1,0.981013,0.996835,0.745253,0.734177,0.459119,0.484177,0.468354,0.996855]
area_under_roc_curve
0.8592207527266934 [0.984177,0.969937,1,0.996855,1,0.984177,0.993711,0.963608,0.734177,1,0.996835,0.974684,0.993711,0.738924,0.996835,0.996835,0.996835,0.707278,0.930818,0.46519,0.993711,0.981132,0.738924,0.993711,0.977987,0.731013,0.952532,0.481132,0.726266,1,0.996855,1,0.702532,0.740506,0.993711,0.490566,0.996855,0.462264,0.996855,0.993671,0.742089,0.481132,1,1,0.732595,0.993711,0.746835,0.996855,0.737342,1,0.721519,0.981132,0.987421,0.993671,0.474843,0.971519,1,0.984177,0.965409,0.990506,0.484277,0.993711,0.992089,0.990566,0.97943,0.484177,0.987342,0.992089,0.45283,0.977848,0.719937,0.996835,0.993711,0.708861,0.45283,0.734177,0.996835,0.677215,0.496835,0.995253,0.465409,1,0.468354,0.987342,1,0.990566,1,0.490506,0.969937,0.993711,1,1,0.477987,0.977848,0.993671,0.996855,0.734177,0.969937,0.446541,0.742089]
area_under_roc_curve
0.8474327780431496 [0.72943,0.987342,0.737342,1,0.740506,0.743671,0.996855,0.998418,0.742089,1,0.742089,0.740506,0.490566,0.998418,0.996835,0.71519,1,0.985759,0.468553,0.732595,0.987421,0.984277,1,0.490566,0.465409,0.950949,0.704114,0.968553,0.990506,0.996835,0.996855,0.490506,0.996835,0.732595,0.984277,0.996855,0.990566,0.465409,0.971698,1,0.976266,0.965409,0.990566,0.996855,0.976266,0.993711,0.75,0.996855,0.735759,0.496835,0.990506,0.996855,0.471698,0.727848,0.974843,0.987342,1,0.75,0.993711,0.726266,0.996855,0.5,0.992089,0.468553,0.990506,0.990506,0.69462,0.477848,0.974843,0.704114,0.424051,0.75,0.993711,0.987342,0.462264,0.474684,1,0.72943,0.996835,0.740506,0.971698,0.740506,0.96519,0.992089,0.996855,0.95283,1,0.949367,0.996835,1,1,0.996855,0.971698,0.981013,0.992089,0.996855,0.727848,0.996835,0.977987,0.481013]
area_under_roc_curve
0.8629809031924209 [0.735759,0.918239,0.743671,0.746835,1,0.993671,0.481132,0.968354,0.996855,1,0.738924,0.977848,0.490566,0.465409,0.990566,0.987342,1,0.735759,0.927673,0.702532,0.987342,1,0.990506,1,0.985759,0.958861,0.949367,0.993711,1,0.996835,1,0.474843,0.990506,0.732595,1,0.988924,0.981013,0.484177,0.745253,0.740506,0.996835,1,0.996855,0.477987,0.990566,0.996855,1,0.995253,0.727848,1,0.946541,0.484277,0.992089,0.484277,0.743671,0.707278,0.996855,0.962025,0.988924,0.474843,0.719937,0.996835,0.987421,1,0.993671,1,0.471698,0.96519,0.716772,0.712025,0.976266,0.996835,1,0.996855,0.72943,0.977987,0.995253,0.949367,0.998418,0.996855,1,1,0.468354,0.992089,0.477987,0.481132,1,1,0.993671,0.740506,1,0.496835,0.490566,0.927673,0.477848,0.481132,0.976266,0.996855,0.968553,0.745253]
area_under_roc_curve
0.8603878074994027 [0.72943,0.993711,0.985759,1,0.996855,0.995253,0.996855,0.726266,1,0.75,0.746835,1,0.990566,0.981132,0.484277,0.985759,1,0.716772,0.984277,0.96519,0.992089,0.737342,0.966772,1,0.976266,0.973101,0.689873,0.962264,0.996855,0.996835,0.474843,0.993711,0.742089,0.993671,0.493711,0.748418,0.716772,0.981013,0.734177,0.993671,0.973101,1,0.490566,0.987421,0.987421,1,1,0.738924,0.996835,0.735759,0.987421,0.996855,0.745253,0.459119,0.713608,0.734177,1,0.977848,0.982595,0.918239,0.993671,0.982595,0.987421,0.468553,0.996835,0.462025,0.465409,0.996835,0.724684,0.734177,0.995253,0.977848,1,0.996855,0.72943,0.955975,0.996835,0.458861,0.995253,0.996855,0.993711,0.740506,0.474684,0.993671,0.974843,1,1,1,0.481013,0.992089,1,0.490506,0.471698,0.990566,0.737342,1,0.993671,0.477987,0.940252,0.993671]
area_under_roc_curve
0.8766255672319083 [0.992089,0.981132,0.471698,0.740506,1,0.996835,0.984177,0.993671,0.993711,1,1,1,0.996835,1,0.996855,0.452532,1,0.981132,0.713608,0.962025,0.990506,0.697785,0.727848,0.993671,0.735759,0.949686,0.996855,0.72943,0.993711,1,0.742089,0.977987,1,0.481132,0.996835,1,0.962025,0.990506,0.996835,1,0.487342,0.721519,1,1,0.996855,0.990506,0.742089,0.745253,0.996855,1,0.474843,0.743671,0.992089,0.977987,0.966772,1,0.75,1,0.987342,0.968553,0.981013,1,0.996855,0.723101,0.740506,0.984277,0.981132,0.726266,0.716772,0.731013,0.443396,0.998418,1,0.993711,0.996835,0.468553,0.996855,0.993711,0.993711,0.465409,0.735759,1,0.726266,0.990506,0.981013,0.958861,1,0.971698,0.984277,0.995253,0.996855,0.996835,0.723101,0.45283,0.949686,0.487342,0.990506,0.993711,0.462025,0.993671]
area_under_roc_curve
0.848601449924369 [1,1,1,0.743671,0.993711,0.746835,1,0.993671,0.487421,1,0.481132,1,0.740506,0.45283,0.496855,1,1,0.468553,0.710443,0.721519,0.982595,0.474684,0.732595,0.996835,0.993671,0.996855,0.965409,0.995253,0.496855,0.732595,0.987342,1,1,0.962264,0.743671,0.998418,0.474684,0.962025,0.740506,0.996855,0.973101,1,0.993671,0.726266,0.468553,1,1,0.981013,0.484277,0.981132,0.977987,0.735759,0.977848,1,0.984177,0.727848,1,0.990506,0.735759,0.455975,0.993671,0.993671,0.987421,0.740506,0.996835,0.990566,0.959119,0.732595,0.721519,0.726266,0.930818,0.996835,0.988924,0.987421,0.72943,1,1,0.45283,0.996855,0.996855,0.724684,0.974843,0.976266,0.981013,0.455696,0.743671,0.990506,0.487421,0.996855,0.996835,0.487421,0.993671,0.993671,0.481132,0.984277,0.743671,0.740506,0.474843,0.462025,0.990506]
area_under_roc_curve
0.8289229808534351 [0.987421,0.996835,1,0.996835,0.477848,0.996855,0.471519,0.996855,0.740506,1,0.984277,1,0.742089,0.977848,0.988924,0.468553,1,0.993711,0.481013,0.481132,1,0.995253,0.977987,0.75,0.742089,0.471698,0.437107,0.971519,0.996835,0.996855,0.988924,0.993671,0.981132,0.981132,0.992089,0.988924,0.974684,0.468354,0.993671,0.5,1,0.738924,0.745253,0.96519,0.732595,0.742089,1,1,0.962264,0.990566,0.992089,0.742089,0.731013,0.734177,0.723101,0.940252,1,0.481132,0.996835,0.46519,0.740506,0.75,0.993671,0.738924,0.490566,0.996855,0.713608,0.990566,0.702532,0.996855,0.930818,0.490566,0.988924,0.484177,0.723101,0.474684,0.996855,0.45283,1,0.740506,0.977848,1,0.471698,0.484277,0.993671,0.984177,0.996835,0.740506,0.481132,1,1,0.996835,0.996835,0.72943,0.474843,0.974684,0.95283,0.985759,0.685127,0.487421]
area_under_roc_curve
0.8442674747233496 [0.465409,0.737342,0.993711,0.745253,1,0.984277,0.990506,0.977987,1,1,0.996855,0.996855,0.987342,0.726266,0.996835,0.490566,0.996855,0.481132,0.976266,0.974843,0.742089,0.677215,0.481132,1,0.992089,0.943396,0.487421,0.710443,0.981013,1,0.745253,1,0.990566,0.984277,0.982595,0.996835,0.742089,0.710443,0.477848,0.996855,0.484277,0.982595,0.740506,0.971519,0.740506,0.740506,0.996855,0.743671,0.996855,0.977987,0.743671,0.995253,0.735759,0.738924,0.996835,0.987421,1,0.487421,0.993671,0.735759,1,1,0.493671,0.743671,0.993711,0.987421,0.96519,0.981132,0.719937,0.487421,0.462264,0.987421,0.468354,0.721519,0.971519,0.977848,0.996855,0.940252,0.996855,0.471519,0.97943,0.984277,0.990566,0.484277,0.96519,0.735759,0.743671,0.977848,1,0.738924,1,0.996835,0.949367,0.72943,0.993711,0.996835,0.459119,0.987342,0.471519,1]
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
0.4063782759709504
kappa
0.48853545917360597
kappa
0.4191690013021347
kappa
0.43795389958951686
kappa
0.4315714680456322
kappa
0.3748766723232961
kappa
0.4567062818336163
kappa
0.41866434974921996
kappa
0.4063782759709504
kappa
0.39374802652352386
kb_relative_information_score
83.03157720951032
kb_relative_information_score
88.95910149620867
kb_relative_information_score
87.55314058158736
kb_relative_information_score
86.3136973545084
kb_relative_information_score
90.98639294542276
kb_relative_information_score
86.25375882448641
kb_relative_information_score
94.59098668497134
kb_relative_information_score
87.54916499549903
kb_relative_information_score
83.56161202939639
kb_relative_information_score
83.6730780353042
mean_absolute_error
0.013660487174438827
mean_absolute_error
0.012684433032959918
mean_absolute_error
0.013378012354810839
mean_absolute_error
0.013411961834362222
mean_absolute_error
0.012915120169204462
mean_absolute_error
0.013625074567156914
mean_absolute_error
0.012602354766607395
mean_absolute_error
0.013335416954056314
mean_absolute_error
0.013485368441328189
mean_absolute_error
0.01367143354502059
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
0.4125
predictive_accuracy
0.49375
predictive_accuracy
0.425
predictive_accuracy
0.44375
predictive_accuracy
0.4375
predictive_accuracy
0.38125
predictive_accuracy
0.4625
predictive_accuracy
0.425
predictive_accuracy
0.4125
predictive_accuracy
0.4
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
0.4125 [1,0.5,1,1,0,0,0.5,1,0,1,0,0,0.5,0,0.5,0,0,0,0.5,0,0,0,0,1,0,0,0,0,0.5,1,0.5,0.5,0.5,0,0.5,0,1,1,1,0.5,0,1,1,0.5,0,1,1,1,1,0.5,0,0.5,0,0,1,1,1,1,0,0,1,1,0,0.5,1,0,1,0,0,0,0,1,0,1,0,1,0.5,0,1,1,0,1,0,1,0,0,0,1,0.5,0,1,1,0,0.5,0,0,0,0,0,1]
recall
0.49375 [0,1,0.5,1,0,0,1,1,0.5,1,0.5,0.5,0.5,0.5,0.5,1,0.5,0.5,0,0,1,0,0,1,1,0,0,0,1,1,0.5,1,0.5,0.5,0,0,0,1,0,1,1,1,1,0.5,0,1,0,1,0.5,1,1,0.5,1,0.5,0,0,1,1,1,0,1,0,0.5,0.5,1,0,0,0,0,1,0,1,0.5,0.5,0,0.5,0.5,0.5,1,0,0,0.5,0,1,0.5,0.5,1,0,1,0,0.5,1,0.5,1,0.5,0,0,0,0,1]
recall
0.425 [1,0,1,0,1,0.5,1,0,0.5,1,1,0.5,1,0.5,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0.5,1,1,1,0,0.5,1,0,1,0,1,1,0.5,0,1,1,0,1,0.5,1,0.5,1,0,0,0,1,0,0,1,0,0,0.5,0,1,0.5,1,0.5,0,0.5,0.5,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0.5,0,1,1,0,0,0,1,0,0,0,0]
recall
0.44375 [0.5,1,0.5,1,0.5,0.5,1,0.5,0.5,1,0.5,0,0,0,1,0.5,0.5,0.5,0,0,0,0,1,0,0,0,0,0,1,1,1,0,1,0,0,1,1,0,0,1,0.5,0,1,1,0,1,0,1,0.5,0,1,1,0,0.5,0,0.5,1,0.5,1,0,1,0,0.5,0,0,1,0,0,0,0,0,0.5,1,0.5,0,0,1,0.5,1,0.5,0,0.5,0.5,0.5,1,0,1,0,1,1,1,1,0,0,0.5,1,0,0,0,0]
recall
0.4375 [0.5,0,0.5,0.5,1,0,0,0,0,1,0.5,0,0,0,0,0,1,0.5,0,0,0.5,1,1,1,0.5,0,0,0,1,0.5,1,0,1,0,1,0.5,0.5,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0.5,1,0,0,0.5,0,0.5,0,1,0,0.5,0,0,1,0,1,0.5,1,0,0,0,0,0,1,1,1,0.5,0,0.5,0,0.5,1,0,1,0,0.5,0,0,1,1,1,0.5,1,0,0,0,0,0,0.5,1,0,0.5]
recall
0.38125 [0,1,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,0,0,0.5,1,0,0,0,0.5,0.5,0.5,1,0.5,0,0,0,1,1,0,1,0.5,0.5,0,0.5,0,0,0,0,0.5,1,0,0,0,1,0.5,0,1,0.5,0,0,0.5,0,0,0,1,0,0.5,0,1,0.5,0,0,1,0,0,0,0,0,0,0,1,1,0.5,0,1,0,0.5,1,0,0.5,0,0.5,0,0,1,1,0,0.5,1,0,0,1,0.5,1,0,0,0,0.5]
recall
0.4625 [0.5,0,0,0.5,1,1,0.5,0.5,0,1,1,1,1,1,1,0,1,0,0,0,1,0,0.5,1,0.5,0,0,0.5,0,1,0.5,0,1,0,1,1,0,0,0,1,0,0,1,1,0,1,0.5,0.5,1,1,0,0.5,0.5,0,0.5,0,0.5,0.5,0,0,0,1,1,0.5,0.5,0,0,0,0.5,0,0,0.5,1,1,0.5,0,1,0,1,0,0.5,1,0.5,0.5,0,0,1,0,0,0.5,1,1,0,0,0,0,1,0,0,1]
recall
0.425 [1,1,1,0.5,1,0.5,1,1,0,1,0,1,0.5,0,0,1,1,0,0,0,0.5,0,0,1,1,1,0,0.5,0,0.5,0,1,1,0,0.5,0.5,0,0,0.5,1,0,1,1,0,0,0,1,0,0,0,0,0.5,0.5,1,0,0.5,1,0,0,0,0.5,1,0,0.5,1,0,0,0,0,0.5,0,1,0.5,0,0,1,1,0,0,1,0,0,0.5,0,0,0.5,1,0,1,1,0,0.5,0.5,0,0,0.5,0,0,0,0.5]
recall
0.4125 [0,1,1,1,0,1,0,1,0.5,1,0,1,0.5,0,0.5,0,1,1,0,0,1,0,0,0.5,0.5,0,0,0,1,1,1,1,0,0,0.5,0.5,0,0,1,0,1,0.5,0.5,0,0,0.5,1,1,0,1,0.5,0.5,0.5,0.5,0,0,1,0,1,0,0,0,0,0.5,0,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0.5,0,1,0,0,1,0.5,1,0.5,0,0,1,1,1,0.5,0,0.5,0,0,0,0]
recall
0.4 [0,0.5,0,0.5,1,0,0.5,0,1,1,1,1,1,0,1,0,1,0,0,0,0.5,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0.5,1,0.5,0,0,0,0,0.5,0.5,0,0,0.5,1,0.5,1,0,0.5,0.5,0.5,0.5,1,0,1,0,1,0.5,1,1,0,0.5,1,0,0,0,0,0,0,0,0,0,0,0.5,1,0,1,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0.5,0,1]
relative_absolute_error
0.6899235946686265
relative_absolute_error
0.6406279309575705
relative_absolute_error
0.6756571896369098
relative_absolute_error
0.6773718098162728
relative_absolute_error
0.6522787964244666
relative_absolute_error
0.6881350791493379
relative_absolute_error
0.6364825639700694
relative_absolute_error
0.6735059067705198
relative_absolute_error
0.6810792142084933
relative_absolute_error
0.6904764416677054
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
0.09245885884161026
root_mean_squared_error
0.08610972901595285
root_mean_squared_error
0.08929802028345613
root_mean_squared_error
0.0895951291328895
root_mean_squared_error
0.08781003514015691
root_mean_squared_error
0.09048409057896747
root_mean_squared_error
0.08584226554272087
root_mean_squared_error
0.08892369312334071
root_mean_squared_error
0.09068558351321693
root_mean_squared_error
0.092549235998263
root_relative_squared_error
0.9292464949153181
root_relative_squared_error
0.8654353392275597
root_relative_squared_error
0.8974788721265748
root_relative_squared_error
0.900464928415862
root_relative_squared_error
0.8825240587509788
root_relative_squared_error
0.9093993271120208
root_relative_squared_error
0.8627472301795727
root_relative_squared_error
0.8937167426146292
root_relative_squared_error
0.9114244072963031
root_relative_squared_error
0.930154819516034
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
111.52186000072106
usercpu_time_millis
111.4599909997196
usercpu_time_millis
117.1666419995745
usercpu_time_millis
114.82076700121979
usercpu_time_millis
118.69550900155446
usercpu_time_millis
114.30325699984678
usercpu_time_millis
97.97134199834545
usercpu_time_millis
98.37971299930359
usercpu_time_millis
96.14630000214675
usercpu_time_millis
98.06697499880102
usercpu_time_millis_testing
1.516405000074883
usercpu_time_millis_testing
1.6003090004232945
usercpu_time_millis_testing
1.5775869997014524
usercpu_time_millis_testing
1.5581570005451795
usercpu_time_millis_testing
1.5728670005046297
usercpu_time_millis_testing
1.3350490007724147
usercpu_time_millis_testing
1.570779999383376
usercpu_time_millis_testing
1.3682389999303268
usercpu_time_millis_testing
1.3382490014919313
usercpu_time_millis_testing
1.32158199994592
usercpu_time_millis_training
110.00545500064618
usercpu_time_millis_training
109.8596819992963
usercpu_time_millis_training
115.58905499987304
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usercpu_time_millis_training
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usercpu_time_millis_training
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usercpu_time_millis_training
96.40056199896208
usercpu_time_millis_training
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usercpu_time_millis_training
94.80805100065481
usercpu_time_millis_training
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