10094018
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)
8019135
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
2
8783
min_samples_split
18
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
57601
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
21108573
description
https://api.openml.org/data/download/21108573/description.xml
-1
21108574
predictions
https://api.openml.org/data/download/21108574/predictions.arff
area_under_roc_curve
0.8222107796717171 [0.799598,0.870956,0.842428,0.933811,0.903192,0.865491,0.804747,0.86922,0.806049,0.841067,0.900055,0.869003,0.805319,0.829841,0.838226,0.734888,0.904928,0.733152,0.764165,0.698982,0.838029,0.866477,0.834182,0.999487,0.804628,0.696141,0.734848,0.805358,0.838029,0.87212,0.902186,0.935705,0.80593,0.789161,0.86847,0.870522,0.766256,0.730804,0.996903,0.934935,0.802064,0.836076,0.840376,0.833116,0.869732,0.770202,0.934422,0.744851,0.702612,0.838108,0.895241,0.705275,0.930496,0.753906,0.80524,0.724708,0.968513,0.899167,0.801057,0.796757,0.778626,0.799361,0.804924,0.646899,0.838522,0.836056,0.762074,0.93602,0.634193,0.698647,0.665246,0.933692,0.771622,0.868943,0.594085,0.900016,0.871133,0.690617,0.903389,0.93241,0.927951,0.706084,0.737196,0.802004,0.830196,0.839331,0.892657,0.766533,0.735105,0.80666,0.934896,0.965179,0.829684,0.729778,0.900864,0.870068,0.862827,0.770912,0.720309,0.769156]
average_cost
0
f_measure
0.44959991486936735 [0.375,0.628571,0.758621,0.692308,0.684211,0.285714,0.451613,0.666667,0.363636,0.6,0.5625,0.645161,0.424242,0.27027,0.628571,0.137931,0.787879,0.2,0.375,0.230769,0.5,0.580645,0.307692,0.941176,0.424242,0.193548,0.133333,0.533333,0.642857,0.647059,0.518519,0.756757,0.5625,0.195122,0.511628,0.645161,0.344828,0.228571,0.777778,0.75,0.4375,0.465116,0.666667,0.470588,0.6875,0.352941,0.592593,0.482759,0.176471,0.588235,0.454545,0.266667,0.387097,0.16,0.516129,0.058824,0.909091,0.473684,0.322581,0.266667,0.64,0.363636,0.461538,0.333333,0.625,0.363636,0.129032,0.727273,0.235294,0.171429,0.102564,0.606061,0.4,0.580645,0.083333,0.555556,0.551724,0.071429,0.769231,0.6,0.6,0.222222,0.413793,0.242424,0.380952,0.62069,0.384615,0.076923,0.25,0.473684,0.685714,0.588235,0.482759,0.1875,0.5,0.588235,0.375,0.388889,0.095238,0.333333]
kappa
0.44696969696969696
kb_relative_information_score
871.4229066534085
mean_absolute_error
0.012417587059590644
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
0.46254892186790497 [0.375,0.578947,0.846154,0.9,0.590909,0.333333,0.466667,0.714286,0.352941,0.642857,0.5625,0.666667,0.411765,0.238095,0.578947,0.153846,0.764706,0.166667,0.375,0.3,0.5,0.6,0.26087,0.888889,0.411765,0.2,0.142857,0.571429,0.75,0.611111,0.636364,0.666667,0.5625,0.16,0.407407,0.666667,0.384615,0.210526,0.7,0.75,0.4375,0.37037,0.714286,0.444444,0.6875,0.333333,0.727273,0.538462,0.166667,0.555556,0.357143,0.285714,0.4,0.222222,0.533333,0.055556,0.882353,0.409091,0.333333,0.285714,0.888889,0.352941,0.391304,0.5,0.625,0.352941,0.133333,0.705882,0.222222,0.157895,0.086957,0.588235,0.368421,0.6,0.125,0.5,0.615385,0.083333,1,0.642857,0.642857,0.272727,0.461538,0.235294,0.307692,0.692308,0.5,0.1,0.375,0.409091,0.631579,0.555556,0.538462,0.1875,0.583333,0.555556,0.375,0.35,0.2,0.5]
predictive_accuracy
0.4525
prior_entropy
6.6438561897747395
recall
0.4525 [0.375,0.6875,0.6875,0.5625,0.8125,0.25,0.4375,0.625,0.375,0.5625,0.5625,0.625,0.4375,0.3125,0.6875,0.125,0.8125,0.25,0.375,0.1875,0.5,0.5625,0.375,1,0.4375,0.1875,0.125,0.5,0.5625,0.6875,0.4375,0.875,0.5625,0.25,0.6875,0.625,0.3125,0.25,0.875,0.75,0.4375,0.625,0.625,0.5,0.6875,0.375,0.5,0.4375,0.1875,0.625,0.625,0.25,0.375,0.125,0.5,0.0625,0.9375,0.5625,0.3125,0.25,0.5,0.375,0.5625,0.25,0.625,0.375,0.125,0.75,0.25,0.1875,0.125,0.625,0.4375,0.5625,0.0625,0.625,0.5,0.0625,0.625,0.5625,0.5625,0.1875,0.375,0.25,0.5,0.5625,0.3125,0.0625,0.1875,0.5625,0.75,0.625,0.4375,0.1875,0.4375,0.625,0.375,0.4375,0.0625,0.25]
relative_absolute_error
0.6271508615954859
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.08917336728307118
root_relative_squared_error
0.8962260623348509
total_cost
0
area_under_roc_curve
0.8306902316694529 [1,0.992089,1,0.996855,0.995253,0.993711,0.745253,1,0.996835,1,0.995253,0.745253,0.738924,0.734177,1,0.949686,0.996835,0.731013,0.487342,0.477987,0.996855,0.481132,0.987421,1,0.984277,0.738924,0.490506,0.990506,0.745253,0.5,0.984177,1,0.743671,0.721519,1,0.487421,1,0.487421,1,1,1,0.493711,0.75,0.993671,0.996835,0.990506,1,1,0.474684,0.737342,0.742089,0.740506,0.984277,0.726266,1,1,0.75,1,0.974843,0.731013,0.490566,0.996855,0.746835,0.493671,1,0.993671,0.982595,1,0.474843,0.977987,0.71519,1,0.743671,0.737342,0.455975,0.992089,0.748418,0.976266,0.746835,0.996835,1,0.490506,0.984277,0.996855,0.735759,0.746835,0.958861,0.726266,1,0.484277,0.740506,1,0.996835,0.984177,0.981013,0.737342,0.996855,0.490506,0.462025,0.996855]
area_under_roc_curve
0.8051573322187725 [1,1,0.746835,1,0.990506,0.993711,0.742089,0.996855,0.746835,1,0.75,0.996835,0.490506,0.743671,1,0.996855,1,0.484177,0.474684,0.990566,1,0.977987,0.987421,1,0.996855,0.721519,0.72943,0.490506,0.996835,1,0.75,1,0.998418,0.735759,0.731013,1,0.471698,0.993711,0.993711,1,0.490566,1,1,0.738924,0.745253,0.737342,0.993711,0.993711,0.474684,1,0.707278,0.455696,0.987421,0.977848,1,0.474843,1,0.474843,0.493711,0.735759,1,0.993711,0.75,0.496835,1,0.974684,0.742089,0.987421,0.471698,0.987421,0.726266,1,0.742089,0.998418,0.449686,0.996835,0.987342,0.724684,0.998418,0.75,0.987342,0.740506,0.468553,0.474843,0.732595,0.737342,0.742089,0.468354,0.745253,0.496855,0.746835,0.993711,0.97943,0.490506,0.992089,0.992089,1,0.490506,0.726266,0.490566]
area_under_roc_curve
0.8194524470583552 [0.738924,0.745253,0.490506,1,0.743671,0.977848,0.493711,1,0.993671,0.746835,0.743671,0.748418,1,0.746835,0.481013,0.735759,0.996835,0.710443,0.481132,0.481013,1,0.981132,0.721519,0.996855,0.493711,0.996835,1,0.996855,1,0.996835,1,0.990506,1,0.716772,0.487421,0.996855,0.996855,0.484277,1,1,0.493671,0.993711,1,0.996855,0.737342,0.471698,0.746835,0.993711,0.740506,0.746835,0.987342,0.996855,0.990566,0.477848,1,0.977848,1,0.990506,0.996855,0.484177,0.496855,0.496855,0.995253,0.996855,0.987342,0.745253,0.75,0.996835,0.487421,0.477848,0.735759,0.993671,0.484277,0.742089,0.481132,1,0.996835,0.481013,0.743671,1,0.484277,0.737342,0.996835,1,0.996855,1,1,0.992089,0.746835,0.996855,0.990506,1,0.484277,0.732595,0.998418,1,0.737342,0.981013,0.487421,0.740506]
area_under_roc_curve
0.8327499303399412 [0.737342,0.998418,1,0.996855,0.748418,0.490506,0.993711,0.984177,0.746835,0.743671,0.977848,0.992089,0.493711,0.987342,0.996835,0.995253,0.746835,0.732595,0.465409,0.46519,0.968553,1,0.75,1,0.490566,0.985759,0.743671,0.996855,0.992089,0.746835,1,0.996835,0.742089,0.724684,0.996855,0.496855,0.996855,0.984277,0.996855,1,0.743671,0.993711,1,0.981132,1,0.996855,0.992089,1,0.968354,0.990506,0.996835,1,0.996855,0.468354,0.996855,0.971519,1,0.998418,1,0.982595,1,0.981132,0.96519,0.490566,0.745253,1,0.734177,1,0.468553,0.732595,0.484177,0.737342,0.993711,1,0.462264,0.746835,0.746835,0.471519,1,0.988924,0.487421,0.487342,1,0.721519,0.968553,0.5,0.996855,0.984177,0.458861,0.984277,1,1,0.471698,0.477848,1,0.5,0.487342,0.996835,0.933962,0.474684]
area_under_roc_curve
0.790025052742616 [0.742089,1,0.75,1,0.493711,0.746835,0.484277,0.490506,0.490566,0.746835,1,0.993671,1,0.468553,0.993711,0.490506,0.75,0.743671,0.996855,0.993671,0.490506,0.718354,1,0.996835,0.743671,0.468354,0.734177,1,1,0.743671,0.487421,1,0.487342,0.462025,0.987421,1,0.724684,0.474684,0.995253,0.990506,0.992089,0.75,0.5,0.993711,0.996855,0.481132,0.998418,0.5,0.748418,0.988924,1,0.487421,0.998418,0.984277,0.743671,0.705696,1,0.72943,0.993671,0.487421,0.748418,0.745253,1,0.496855,0.745253,0.477848,0.474843,1,0.477848,0.719937,0.735759,0.996835,0.996855,0.496855,0.976266,1,0.746835,0.708861,1,0.990566,0.993711,0.996835,0.477848,0.738924,1,0.996855,0.487421,1,1,0.740506,0.996855,0.987342,0.95283,0.987421,0.748418,1,0.734177,0.993711,0.468553,0.738924]
area_under_roc_curve
0.8266197207626778 [0.740506,1,1,0.990506,1,0.993671,0.996855,0.745253,0.993711,0.745253,0.987342,1,0.987421,0.984277,1,0.743671,1,0.987342,0.949686,0.474684,0.75,0.740506,0.75,1,0.734177,0.726266,0.718354,1,1,1,1,1,0.735759,0.987342,0.490566,0.996835,0.735759,0.731013,1,0.996835,0.996835,0.735759,1,0.996855,0.996855,0.984277,1,0.746835,0.471519,0.745253,0.993711,1,0.982595,0.968553,0.734177,0.484177,0.993711,0.996835,0.993671,0.996855,0.745253,0.735759,0.493711,0.965409,0.743671,0.743671,0.468553,1,0.727848,0.745253,0.474684,0.990506,0.949686,0.996855,0.468354,0.974843,0.743671,0.713608,0.993671,0.493711,0.996855,0.487342,0.735759,0.474684,0.487421,1,0.977987,0.493711,0.484177,0.990506,1,0.987342,0.487421,0.993711,0.993671,0.990566,0.982595,0.484277,0.465409,0.742089]
area_under_roc_curve
0.8336788969827241 [0.988924,0.493711,1,0.995253,1,0.993671,1,1,0.990566,0.740506,1,0.996855,0.740506,0.993711,0.493711,0.738924,0.5,0.477987,0.723101,0.976266,0.742089,0.996835,0.984177,1,0.737342,0.481132,0.968553,0.742089,0.487421,1,0.998418,0.5,0.996855,0.993711,1,0.75,0.990506,0.738924,0.995253,0.5,0.735759,0.987342,0.740506,0.971519,0.996855,0.738924,0.746835,0.748418,0.993711,1,0.990566,0.738924,1,0.965409,0.738924,0.723101,1,0.740506,0.740506,0.962264,1,0.702532,0.993711,0.493671,0.743671,0.990566,0.468553,0.996835,0.72943,0.468354,0.990566,1,0.732595,1,0.46519,0.990566,1,0.971698,1,0.996855,0.995253,0.993711,0.740506,0.987342,1,0.740506,0.727848,0.468553,0.993711,0.5,0.996855,0.996835,0.735759,0.474843,0.490566,0.743671,0.995253,0.493711,0.727848,0.998418]
area_under_roc_curve
0.8104236326725575 [0.742089,0.993711,1,0.484177,0.996855,0.732595,0.993671,0.996835,0.493711,0.998418,1,0.490566,0.742089,0.981132,0.481132,0.474684,1,0.477987,0.993671,0.487342,0.990506,0.738924,0.72943,1,0.984177,0.459119,0.471698,0.745253,1,0.742089,0.745253,0.996855,1,1,0.740506,1,0.740506,0.977848,0.988924,0.996855,0.996835,0.737342,0.993671,0.490506,1,0.745253,0.998418,1,0.965409,0.5,0.974843,0.481013,0.487342,1,0.738924,0.72943,1,0.996835,0.731013,0.990566,0.5,0.738924,0.993711,0.742089,0.75,0.990566,0.993711,0.75,0.487342,0.734177,0.471698,0.748418,0.996835,0.477987,0.727848,1,0.993711,0.937107,1,0.955975,0.958861,0.981132,0.481013,0.996835,0.46519,0.740506,0.990506,0.490566,0.987421,0.740506,1,0.996835,0.738924,0.974843,0.481132,1,0.981013,0.990566,0.947785,0.72943]
area_under_roc_curve
0.8282250119417244 [0.477987,1,1,1,1,0.996855,0.740506,0.996855,0.738924,1,0.490566,0.996855,0.996835,0.745253,0.996835,0.484277,0.996855,0.981132,1,0.968553,0.748418,1,0.987421,1,0.75,0.45283,0.465409,0.743671,0.742089,0.996855,0.996835,1,0.481132,0.981132,0.993671,1,0.738924,0.735759,1,0.5,0.487421,0.743671,0.746835,0.738924,0.490506,0.748418,0.996855,0.487342,0.484277,0.996855,0.740506,0.726266,0.996835,0.719937,0.746835,0.481132,1,0.977987,0.742089,0.724684,0.745253,0.996835,0.734177,0.742089,1,0.987421,0.988924,0.5,0.731013,0.984277,0.984277,0.987421,0.745253,1,0.471519,0.484177,0.996855,0.481132,1,0.996835,1,0.996855,0.481132,0.987421,0.985759,1,0.962025,0.738924,0.462264,0.996835,1,0.75,1,0.468354,1,0.734177,0.943396,0.998418,0.731013,1]
area_under_roc_curve
0.8444418736565561 [0.962264,0.493671,0.496855,0.996835,1,0.987421,0.740506,0.481132,0.737342,1,0.996855,0.484277,0.996835,0.987342,0.742089,1,1,1,0.996835,1,1,0.996835,0.496855,1,1,0.455975,1,0.743671,0.487342,0.993711,0.996835,0.75,1,0.927673,0.992089,0.740506,0.484177,0.735759,0.996835,1,0.971698,0.987342,0.748418,0.743671,1,0.738924,0.993711,0.481013,0.993711,0.490566,0.995253,0.737342,0.996835,0.724684,0.746835,0.468553,1,0.993711,0.493671,1,1,0.731013,0.487342,0.742089,0.993711,0.5,0.721519,0.996855,0.987342,0.471698,0.468553,0.987421,0.490506,0.996835,0.734177,0.996835,1,0.471698,0.493711,0.996835,0.993671,0.490566,0.996855,0.493711,0.982595,1,0.982595,0.982595,0.45283,0.998418,1,0.996835,0.996835,0.990506,1,1,1,0.743671,0.988924,0.996855]
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
0.43150414528227393
kappa
0.43806479618010336
kappa
0.4694667035092566
kappa
0.44418127269856306
kappa
0.4253236501420903
kappa
0.41907731165397216
kappa
0.425414364640884
kappa
0.39374802652352386
kappa
0.5073230429118472
kappa
0.5136586136112428
kb_relative_information_score
88.48376028611763
kb_relative_information_score
84.48031536579148
kb_relative_information_score
87.94237016229745
kb_relative_information_score
88.69699178341618
kb_relative_information_score
80.12347993731437
kb_relative_information_score
85.7055598725114
kb_relative_information_score
88.60185665605373
kb_relative_information_score
81.6982627042156
kb_relative_information_score
91.34458627400423
kb_relative_information_score
94.34572361169575
mean_absolute_error
0.012548486888928068
mean_absolute_error
0.012338642148292887
mean_absolute_error
0.01193583088684192
mean_absolute_error
0.012462215521406693
mean_absolute_error
0.012801459947323924
mean_absolute_error
0.012962306770027358
mean_absolute_error
0.012627452510724576
mean_absolute_error
0.013238205758702094
mean_absolute_error
0.01159651331103537
mean_absolute_error
0.0116647568526245
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
0.4375
predictive_accuracy
0.44375
predictive_accuracy
0.475
predictive_accuracy
0.45
predictive_accuracy
0.43125
predictive_accuracy
0.425
predictive_accuracy
0.43125
predictive_accuracy
0.4
predictive_accuracy
0.5125
predictive_accuracy
0.51875
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
0.4375 [1,0.5,1,1,1,1,0.5,1,0,1,0.5,0.5,0,0,1,0,1,0,0,0,1,0,1,1,0,0.5,0,0.5,0.5,0,0,1,0.5,0,1,0,0,0,1,1,1,0,0.5,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,0.5,1,0,0,0,1,0.5,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,1,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,0.5,1,1,0,0,0.5,0,0,0,1]
recall
0.44375 [1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,0,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0.5,1,0,1,1,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0,1,0.5,1,0,1,0,0,0,1,1,0.5,0,1,0,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0.5,0,0,0,0.5,1,0.5,0,0.5,0.5,1,0,0,0]
recall
0.475 [0,0.5,0,1,0.5,0,0,1,0.5,0.5,0.5,0.5,1,0.5,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0.5,1,0.5,1,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0.5,1,0,0.5,0,0,1,0,0.5,1,0,0,1,1,1,0,1,0,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5]
recall
0.45 [0.5,1,1,0,0.5,0,1,0.5,0,0,0.5,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0.5,0.5,1,1,0.5,0.5,1,0,1,0,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,1,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0]
recall
0.43125 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,1,0.5,1,0,1,0,0.5,0.5,1,1,0,0,1,1,0.5,0,0,1,1,0,0,1,0,0,1,1,0.5,0,0.5,1,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,1,1,0,0.5,0,1,0,1,1,0,1,1,0,1,0.5,1,0.5,1,0,0]
recall
0.425 [0,1,1,0,1,0,0,0.5,1,0.5,0.5,1,0,1,1,0,1,0.5,0,0,0.5,0.5,0,1,0.5,0,0,1,1,1,1,1,0.5,0,0,0,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,1,0,0,0,0,1,1,1,1,0.5,0,0,0,0.5,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,0.5,0,0.5,0,0,0.5]
recall
0.43125 [0.5,0,1,0.5,1,0,1,1,1,0,1,1,0.5,1,0,0,0,0,0,0,0.5,1,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,1,0,0,1,0.5,0,1,0.5,0,0.5,0,1,1,0,0.5,0,0.5,0,1,0.5,0,0,1,0,1,0,0.5,1,0,1,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,0.5,0,0,0,0,1,0.5,0.5,0,0,0.5,0.5,0,0.5,0]
recall
0.4 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0.5,0,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,0,0,0,0,1,0.5,0,1,0.5,0.5,1,0,0.5,1,0.5,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0]
recall
0.5125 [0,1,1,1,1,1,0.5,0,0.5,1,0,1,1,0.5,1,0,1,0,1,0,0.5,1,1,1,0.5,0,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0,0.5,0,0.5,1,0,0,1,0.5,0,1,0,0,0,1,0,0,0,0,1,0.5,0.5,1,0,0.5,0,0.5,1,1,0,0.5,1,0,0,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,1,1,0,0.5,0,1,0.5,0,1,0,1]
recall
0.51875 [0,0,0,0.5,1,0,0,0,0.5,1,1,0,1,0,0.5,1,1,1,0.5,1,1,1,0,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,1,1,0,0.5,0.5,0.5,1,0.5,0,0,1,0,1,0,1,0,0.5,0,1,1,0,1,1,0,0,0.5,1,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0,1,0.5,0,1,0,1,1,0,0,0,1,1,1,1,0.5,0,1,1,0.5,0,0]
relative_absolute_error
0.6337619640872751
relative_absolute_error
0.6231637448632761
relative_absolute_error
0.602819741759692
relative_absolute_error
0.6294048243134682
relative_absolute_error
0.6465383811779749
relative_absolute_error
0.6546619580821887
relative_absolute_error
0.6377501268042706
relative_absolute_error
0.6685962504394984
relative_absolute_error
0.5856824904563309
relative_absolute_error
0.5891291339709334
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
0.08919832698161988
root_mean_squared_error
0.08891968063359783
root_mean_squared_error
0.08969157069354591
root_mean_squared_error
0.0875444435254731
root_mean_squared_error
0.0924612532843368
root_mean_squared_error
0.09072799389695693
root_mean_squared_error
0.08954843008577576
root_mean_squared_error
0.09209685241399575
root_mean_squared_error
0.08609051564249966
root_mean_squared_error
0.08517191477003264
root_relative_squared_error
0.8964769167438402
root_relative_squared_error
0.8936764155753798
root_relative_squared_error
0.901434202570085
root_relative_squared_error
0.879854762589249
root_relative_squared_error
0.9292705599701844
root_relative_squared_error
0.9118506476904865
root_relative_squared_error
0.8999955853330032
root_relative_squared_error
0.9256081934241231
root_relative_squared_error
0.8652422375587682
root_relative_squared_error
0.8560099514191737
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
285.75902600096015
usercpu_time_millis
273.99368199985474
usercpu_time_millis
216.50593600134016
usercpu_time_millis
216.64433700061636
usercpu_time_millis
217.74785299930954
usercpu_time_millis
210.36406500206795
usercpu_time_millis
214.48399500150117
usercpu_time_millis
222.27989400016668
usercpu_time_millis
221.17503799927363
usercpu_time_millis
225.8276680004201
usercpu_time_millis_testing
1.8022340009338222
usercpu_time_millis_testing
1.7890369999804534
usercpu_time_millis_testing
1.4632580005127238
usercpu_time_millis_testing
1.4572180007235147
usercpu_time_millis_testing
1.4073229995119618
usercpu_time_millis_testing
1.389356000800035
usercpu_time_millis_testing
1.455780000469531
usercpu_time_millis_testing
1.7724749995977618
usercpu_time_millis_testing
1.5014439995866269
usercpu_time_millis_testing
1.6333120001945645
usercpu_time_millis_training
283.9567920000263
usercpu_time_millis_training
272.2046449998743
usercpu_time_millis_training
215.04267800082744
usercpu_time_millis_training
215.18711899989285
usercpu_time_millis_training
216.34052999979758
usercpu_time_millis_training
208.9747090012679
usercpu_time_millis_training
213.02821500103164
usercpu_time_millis_training
220.50741900056892
usercpu_time_millis_training
219.673593999687
usercpu_time_millis_training
224.19435600022553