10132499
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)
8057971
axis
0
8778
copy
true
8778
missing_values
"NaN"
8778
strategy
"mean"
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
13
8783
min_samples_split
20
8783
min_weight_fraction_leaf
0.0
8783
presort
false
8783
random_state
16258
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
21185536
description
https://api.openml.org/data/download/21185536/description.xml
-1
21185537
predictions
https://api.openml.org/data/download/21185537/predictions.arff
area_under_roc_curve
0.8465425741792931 [0.829447,0.869042,0.904257,0.892677,0.899384,0.856001,0.828677,0.896642,0.833314,0.804411,0.926156,0.929352,0.832722,0.825245,0.835701,0.818497,0.903823,0.815795,0.793383,0.75578,0.863498,0.833195,0.865017,0.999744,0.80015,0.817077,0.821911,0.834103,0.864958,0.870245,0.953086,0.902383,0.832919,0.781704,0.930496,0.870028,0.793383,0.751677,0.958077,0.9651,0.765132,0.860184,0.839173,0.861072,0.835642,0.75947,0.932232,0.804293,0.720151,0.832406,0.953697,0.820983,0.924045,0.812993,0.795553,0.684876,0.966955,0.926215,0.889106,0.821753,0.77326,0.79218,0.926748,0.636955,0.837634,0.925406,0.81757,0.933436,0.658953,0.719322,0.745778,0.931818,0.835385,0.864051,0.702869,0.929036,0.901121,0.744949,0.896544,0.9273,0.896327,0.763356,0.761719,0.822798,0.857323,0.866951,0.884411,0.849057,0.73108,0.769137,0.965653,0.96151,0.887468,0.785077,0.895537,0.862334,0.891256,0.893782,0.791963,0.852214]
average_cost
0
f_measure
0.36391506935362117 [0.37037,0.615385,0.785714,0.307692,0.457143,0.142857,0.315789,0.516129,0.285714,0.363636,0.37037,0.585366,0.486486,0.193548,0.564103,0.2,0.648649,0.170213,0.4,0.095238,0.432432,0.516129,0.358974,0.914286,0.424242,0.074074,0.195122,0.461538,0.592593,0.555556,0.24,0.702703,0.358974,0.102564,0.5,0.705882,0.296296,0.176471,0.222222,0.697674,0.275862,0.277778,0.666667,0.4375,0.625,0.129032,0.636364,0.428571,0.071429,0.5,0.409091,0.210526,0.315789,0.173913,0.357143,0,0.857143,0.439024,0.214286,0.3,0.387097,0.275862,0.418605,0.083333,0.571429,0.411765,0.166667,0.628571,0.176471,0.052632,0,0.645161,0.4375,0.432432,0.083333,0.555556,0.545455,0,0.555556,0.347826,0.538462,0.222222,0.322581,0.125,0.378378,0.555556,0.307692,0.086957,0,0.086957,0.717949,0.444444,0.333333,0.105263,0.285714,0.333333,0.4,0.258065,0,0.387097]
kappa
0.3768939393939394
kb_relative_information_score
851.0185443256705
mean_absolute_error
0.014146917822849905
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
0.3785108755242095 [0.454545,0.521739,0.916667,0.4,0.421053,0.166667,0.272727,0.533333,0.333333,0.666667,0.454545,0.48,0.428571,0.2,0.478261,0.214286,0.571429,0.129032,0.428571,0.2,0.380952,0.533333,0.304348,0.842105,0.411765,0.090909,0.16,0.391304,0.727273,0.5,0.333333,0.619048,0.304348,0.086957,0.45,0.666667,0.363636,0.166667,1,0.555556,0.307692,0.25,0.647059,0.4375,0.625,0.133333,0.5,0.5,0.083333,0.416667,0.321429,0.181818,0.272727,0.285714,0.416667,0,0.789474,0.36,0.25,0.75,0.4,0.307692,0.333333,0.125,0.526316,0.388889,0.15,0.578947,0.166667,0.045455,0,0.666667,0.4375,0.380952,0.125,0.5,0.529412,0,0.5,0.571429,0.7,0.272727,0.333333,0.125,0.333333,0.5,0.4,0.142857,0,0.142857,0.608696,0.4,0.5,0.333333,0.333333,0.269231,0.368421,0.266667,0,0.4]
predictive_accuracy
0.383125
prior_entropy
6.6438561897747395
recall
0.383125 [0.3125,0.75,0.6875,0.25,0.5,0.125,0.375,0.5,0.25,0.25,0.3125,0.75,0.5625,0.1875,0.6875,0.1875,0.75,0.25,0.375,0.0625,0.5,0.5,0.4375,1,0.4375,0.0625,0.25,0.5625,0.5,0.625,0.1875,0.8125,0.4375,0.125,0.5625,0.75,0.25,0.1875,0.125,0.9375,0.25,0.3125,0.6875,0.4375,0.625,0.125,0.875,0.375,0.0625,0.625,0.5625,0.25,0.375,0.125,0.3125,0,0.9375,0.5625,0.1875,0.1875,0.375,0.25,0.5625,0.0625,0.625,0.4375,0.1875,0.6875,0.1875,0.0625,0,0.625,0.4375,0.5,0.0625,0.625,0.5625,0,0.625,0.25,0.4375,0.1875,0.3125,0.125,0.4375,0.625,0.25,0.0625,0,0.0625,0.875,0.5,0.25,0.0625,0.25,0.4375,0.4375,0.25,0,0.375]
relative_absolute_error
0.7144907991338323
root_mean_prior_squared_error
0.09949874371066209
root_mean_squared_error
0.09029184812544229
root_relative_squared_error
0.9074672177571101
total_cost
0
area_under_roc_curve
0.8572589513175704 [0.996855,0.996835,1,0.981132,0.990506,0.962264,1,0.996855,0.996835,0.996855,0.988924,0.738924,0.732595,0.968354,0.996835,0.990566,0.996835,0.727848,0.487342,0.477987,0.993711,0.490566,0.987421,1,0.984277,0.738924,0.731013,0.988924,0.738924,0.493711,0.949367,1,0.743671,0.710443,1,0.490566,0.981132,0.477987,0.987421,1,0.996855,0.977987,1,0.996835,0.996835,0.976266,1,0.996855,0.471519,0.743671,0.738924,0.737342,0.981132,0.723101,0.996855,0.959119,0.996835,1,0.968553,0.724684,0.490566,0.996855,0.745253,0.474684,1,0.993671,0.984177,0.996855,0.462264,0.946541,0.683544,1,0.740506,0.734177,0.990566,0.987342,0.996835,0.982595,0.740506,0.990506,0.743671,0.477848,0.987421,0.996855,0.734177,0.738924,0.924051,0.72943,0.998418,0.471698,0.987342,0.996855,0.996835,0.963608,0.982595,0.735759,0.993711,0.992089,0.446203,0.996855]
area_under_roc_curve
0.8642642653849212 [1,0.996835,0.745253,1,0.990506,0.993711,0.735759,0.996855,0.734177,1,0.995253,0.742089,0.745253,0.740506,0.996835,0.996855,0.995253,0.966772,0.735759,0.981132,0.490566,0.95283,0.984277,1,1,0.721519,0.969937,0.484177,0.993671,0.996855,0.998418,0.996835,0.984177,0.737342,0.981013,1,0.459119,0.959119,0.981132,0.996835,0.993711,1,0.996835,0.987342,0.740506,0.731013,0.996855,0.990566,0.458861,0.990506,0.954114,0.724684,0.974843,0.977848,1,0.471698,1,0.993711,0.987421,0.713608,1,0.993711,0.996835,0.496835,1,0.993671,0.735759,0.974843,0.45283,0.955975,0.936709,1,0.743671,0.993671,0.933962,0.996835,0.984177,0.700949,0.988924,0.75,0.723101,0.735759,0.459119,0.446541,0.981013,0.713608,0.735759,0.958861,0.724684,0.490566,0.746835,0.996855,0.960443,0.716772,0.977848,0.992089,0.996855,0.732595,0.914557,0.996855]
area_under_roc_curve
0.8415140365018706 [0.726266,0.742089,0.957278,1,0.743671,0.954114,0.493711,0.998418,0.957278,0.727848,0.710443,0.993671,1,0.746835,0.471519,0.724684,0.996835,0.691456,0.474843,0.716772,1,0.984277,0.723101,0.996855,0.481132,0.996835,0.990506,0.996855,1,0.993671,0.962264,0.990506,0.988924,0.723101,0.468553,0.996855,0.996855,0.474843,1,1,0.490506,0.993711,1,0.996855,0.735759,0.459119,0.746835,0.987421,0.723101,0.745253,0.987342,0.996855,0.984277,0.726266,0.990566,0.993671,1,0.988924,0.993711,0.468354,0.490566,0.490566,0.995253,0.996855,0.988924,0.735759,0.973101,0.996835,0.477987,0.468354,0.990506,0.993671,0.493711,0.742089,0.462264,0.974684,0.993671,0.462025,0.742089,1,0.990566,0.738924,0.996835,0.987342,0.996855,0.990566,1,0.992089,0.745253,0.996855,0.992089,0.996855,0.977987,0.740506,0.993671,0.987421,0.732595,0.97943,0.446541,0.731013]
area_under_roc_curve
0.8496326675821985 [0.742089,0.998418,1,0.990566,0.742089,0.490506,0.993711,0.977848,0.746835,0.743671,0.993671,0.977848,0.496855,0.988924,0.996835,0.981013,0.743671,0.962025,0.462264,0.455696,0.955975,1,0.746835,1,0.490566,0.984177,0.737342,0.990566,0.995253,0.746835,0.993711,0.996835,0.742089,0.974684,0.996855,0.496855,0.996855,0.91195,0.993711,0.993671,0.731013,0.990566,0.996855,0.968553,0.993671,0.996855,0.996835,1,0.974684,0.990506,0.996835,0.987421,0.471698,0.452532,0.996855,0.96519,1,0.985759,0.993711,0.985759,0.990566,0.974843,0.96519,0.481132,0.746835,0.981013,0.734177,1,0.987421,0.726266,0.712025,0.737342,1,0.996835,0.899371,0.746835,0.746835,0.481013,0.996835,0.974684,0.487421,0.962025,1,0.702532,0.899371,0.496855,0.996855,0.993671,0.443038,0.490566,1,0.993711,0.471698,0.471519,0.992089,0.496855,0.735759,0.993671,0.946541,0.705696]
area_under_roc_curve
0.8099699715388902 [0.738924,1,0.75,1,0.490566,0.743671,0.481132,0.738924,0.487421,0.5,0.993671,0.984177,0.996855,0.468553,0.993711,0.985759,0.75,0.993671,0.996855,0.987342,0.471519,0.71519,1,0.996835,0.993671,0.71519,0.969937,1,1,0.743671,0.990566,0.468553,0.487342,0.455696,1,1,0.716772,0.46519,0.737342,0.987342,0.992089,0.745253,0.5,0.993711,0.996855,0.477987,0.996835,0.493671,0.738924,0.982595,0.974843,0.471698,0.981013,0.946541,0.740506,0.455696,0.996855,0.727848,0.988924,0.481132,0.745253,0.738924,0.990566,0.493711,0.738924,0.936709,0.462264,0.987342,0.471519,0.716772,0.731013,0.985759,1,0.490566,0.973101,1,0.746835,0.933544,0.990506,0.981132,0.993711,0.996835,0.477848,0.738924,0.993711,1,0.487421,1,1,0.738924,0.996855,0.987342,0.946541,0.984277,0.748418,0.990566,0.734177,0.993711,0.940252,0.734177]
area_under_roc_curve
0.8435407710373376 [0.72943,0.996855,1,0.96519,0.993711,0.97943,0.977987,0.738924,1,0.742089,0.97943,1,0.993711,0.943396,0.996855,0.726266,1,0.987342,0.955975,0.726266,1,0.737342,0.743671,0.996835,0.735759,0.985759,0.724684,1,1,0.993671,0.977987,1,0.72943,0.731013,0.481132,1,0.738924,0.726266,0.988924,0.996835,0.992089,0.732595,1,0.996855,0.996855,0.984277,0.996835,0.5,0.724684,0.738924,0.981132,0.968553,0.977848,0.993711,0.710443,0.468354,0.993711,0.996835,0.993671,0.993711,0.745253,0.727848,0.477987,0.984277,0.746835,0.996835,0.468553,0.996835,0.723101,1,0.471519,0.990506,0.949686,0.996855,0.672468,0.987421,0.743671,0.713608,0.990506,0.490566,0.996855,0.487342,0.727848,0.719937,0.484277,0.993711,0.959119,0.468553,0.468354,0.990506,0.996855,0.981013,0.487421,0.981132,0.988924,0.962264,0.982595,0.484277,0.893082,0.737342]
area_under_roc_curve
0.8391663681235568 [0.987342,0.481132,1,0.691456,1,0.993671,0.996835,0.996835,0.990566,0.734177,1,0.996855,0.727848,0.993711,0.490566,0.713608,0.493711,0.465409,0.705696,0.97943,0.738924,0.742089,0.984177,1,0.727848,0.468553,0.993711,0.742089,0.959119,1,0.985759,0.493711,0.996855,0.933962,1,0.743671,0.984177,0.734177,0.995253,0.493711,0.477848,0.981013,0.740506,0.990506,0.484277,0.960443,0.740506,0.745253,0.95283,0.993711,0.987421,0.974684,0.988924,0.965409,0.727848,0.704114,1,0.737342,0.987342,0.946541,0.987342,0.699367,0.984277,0.481013,0.743671,0.990566,0.462264,0.996835,0.726266,0.455696,0.990566,1,0.981013,0.990566,0.427215,0.996855,0.996855,0.971698,0.993711,0.993711,0.995253,0.990566,0.731013,0.960443,0.996835,0.993671,0.723101,0.468553,1,0.75,0.996855,0.990506,0.72943,0.987421,0.490566,0.735759,0.984177,0.487421,0.710443,0.985759]
area_under_roc_curve
0.8297950999124273 [1,0.993711,1,0.474684,1,0.716772,0.984177,0.996835,0.493711,0.996835,0.984277,1,0.732595,0.484277,0.477987,0.458861,1,0.462264,0.971519,0.477848,0.977848,0.735759,0.727848,1,0.987342,0.977987,0.427673,0.735759,1,0.738924,0.981013,0.996855,1,0.984277,0.981013,1,0.735759,0.987342,0.984177,0.996855,0.72943,0.727848,0.993671,0.484177,1,0.732595,0.996835,1,0.924528,0.484277,0.974843,0.71519,0.735759,0.993711,0.735759,0.710443,1,0.992089,0.726266,0.990566,0.490506,0.732595,0.987421,0.727848,0.743671,0.984277,0.974843,0.743671,0.46519,0.724684,0.455975,1,0.996835,0.474843,0.718354,1,0.987421,0.908805,1,0.955975,0.971519,1,0.719937,0.996835,0.46519,0.737342,0.990506,0.481132,1,0.727848,1,0.993671,0.987342,0.968553,0.462264,0.993671,0.987342,0.990566,0.693038,0.973101]
area_under_roc_curve
0.869348827720723 [0.471698,0.996835,1,1,0.996835,0.990566,0.724684,0.974843,0.974684,1,0.481132,0.996855,0.996835,0.743671,0.996835,0.981132,0.993711,0.474843,1,1,0.993671,1,0.993711,1,0.745253,0.981132,0.474843,0.990506,0.72943,1,0.990506,0.996835,0.943396,0.962264,0.996835,1,0.735759,0.718354,0.993671,0.996855,0.484277,0.726266,0.496835,0.735759,0.487342,0.490506,0.990566,0.97943,0.455975,0.996855,0.987342,0.984177,0.990506,0.700949,0.734177,0.465409,0.75,0.962264,0.971519,0.973101,0.745253,0.990506,0.981013,0.723101,0.996855,1,0.985759,0.5,0.72943,0.987421,0.987421,0.981132,0.743671,0.993671,0.436709,0.732595,0.996855,0.996855,1,0.993671,0.996835,0.987421,0.468553,0.981132,0.981013,0.996835,0.946203,0.941456,0.459119,0.987342,1,0.746835,0.996835,0.707278,0.993711,0.724684,0.955975,0.992089,0.96519,0.996855]
area_under_roc_curve
0.8573420458164154 [0.987421,0.481013,0.496855,0.971519,0.993671,0.987421,0.708861,0.481132,0.735759,0.987421,0.996855,0.993711,0.996835,0.987342,0.740506,1,1,0.981132,0.993671,0.996855,1,0.996835,1,1,0.742089,0.45283,0.996855,0.743671,0.484177,0.996855,0.738924,0.75,1,0.959119,0.987342,0.732595,0.727848,0.985759,0.984177,1,0.974843,0.969937,0.746835,0.738924,1,0.738924,0.993711,0.732595,0.984277,0.474843,0.990506,0.704114,0.993671,0.966772,0.737342,0.462264,1,0.987421,0.468354,1,0.996835,0.724684,0.992089,0.719937,0.990566,0.5,0.933544,0.993711,0.985759,0.45283,0.455975,0.493711,0.738924,0.990506,0.726266,0.996835,1,0.468553,0.477987,0.996835,1,0.481132,0.996855,0.477987,0.973101,1,0.969937,0.938291,0.45283,0.727848,1,0.996835,0.996835,0.72943,1,0.993671,0.996855,1,0.992089,0.962264]
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
0.4251648308263256
kappa
0.3998736576121289
kappa
0.35588270118798593
kappa
0.3998499624906226
kappa
0.3682381742083235
kappa
0.3373826615129763
kappa
0.33706889748244023
kappa
0.3370427370664141
kappa
0.393939393939394
kappa
0.4129019530479385
kb_relative_information_score
88.60882141925181
kb_relative_information_score
87.71207515577218
kb_relative_information_score
84.76769061221341
kb_relative_information_score
85.83133626569423
kb_relative_information_score
77.9937189525235
kb_relative_information_score
83.81596519459168
kb_relative_information_score
81.19637991480036
kb_relative_information_score
80.63627574632049
kb_relative_information_score
90.20899451525703
kb_relative_information_score
90.24728654925337
mean_absolute_error
0.013673977122883105
mean_absolute_error
0.014114910007507933
mean_absolute_error
0.014059743330546609
mean_absolute_error
0.014210277909043178
mean_absolute_error
0.014351618979202101
mean_absolute_error
0.014329994692106812
mean_absolute_error
0.014757453420930341
mean_absolute_error
0.014438545653202444
mean_absolute_error
0.013878962157782272
mean_absolute_error
0.013653694955294851
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
0.43125
predictive_accuracy
0.40625
predictive_accuracy
0.3625
predictive_accuracy
0.40625
predictive_accuracy
0.375
predictive_accuracy
0.34375
predictive_accuracy
0.34375
predictive_accuracy
0.34375
predictive_accuracy
0.4
predictive_accuracy
0.41875
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
0.43125 [0,1,1,0,0.5,0,1,1,1,0,0.5,0.5,0,0,1,1,1,0,0,0,1,0,1,1,0,0.5,0,0.5,0.5,0,0,1,0.5,0,1,0,0,0,0,1,1,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,1,0,0.5,0,0.5,0,0,1,0.5,0.5,0,0,0,0,1,0,1,0,0,0.5,0,0.5,0,1]
recall
0.40625 [1,1,0.5,1,0,0,0.5,1,0,1,0.5,0.5,0.5,0.5,1,0,0.5,0,0.5,0,0,0,0,1,1,0,0.5,0,0.5,1,0.5,1,0.5,0,0,1,0,0,0,1,1,1,1,0,0.5,0,1,0,0,1,0,0.5,0,0.5,1,0,1,0,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0,0.5,0.5,0,0,0,0,1,0,0.5,0,0,0,0.5,1,0,0,0.5,0.5,1,0,0,1]
recall
0.3625 [0,0.5,0,1,0.5,0,0,0.5,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,0,1,1,1,0,1,0.5,0,0,1,0,0,1,1,0,0,1,1,0,0,0.5,0,0,0.5,1,1,0,0,0,0,1,0.5,1,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0,1,0,0.5,0,0,1,0,0.5,1,0,0,1,0,1,0,0,0,0,1,0.5,1,0,0.5,0.5,1,0,0.5,0,0.5]
recall
0.40625 [0.5,1,1,0,0.5,0,1,0,0,0,1,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0,0.5,1,1,0.5,0.5,1,0,1,0,0,1,0,0,1,0,1,1,1,1,0,1,1,1,0,0,1,0,1,1,0,0.5,1,0,1,0,0.5,0,0.5,1,0,0.5,0,0,1,1,0,0.5,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0.5,0,0,0]
recall
0.375 [0.5,1,0.5,1,0,0.5,0,0,0,0,0,0.5,1,0,1,0.5,0.5,1,1,0,0,0,1,1,1,0,0.5,1,1,0.5,0,0,0,0,1,1,0.5,0,0,1,1,0.5,0,1,1,0,1,0,0,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0,0,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,0,1,0,0,1,1,0,0,0.5,0,0.5,1,0,0]
recall
0.34375 [0,1,1,0,1,0,0,0.5,1,0.5,0,1,1,0,1,0,1,0.5,0,0,1,0.5,0,1,0.5,0,0.5,1,1,0.5,0,1,0,0,0,1,0,0.5,0,1,0,0.5,1,1,1,0,1,0,0,0.5,1,0,0,0,0,0,1,1,0,0,0.5,0,0,0,0.5,0.5,0,1,0,0,0,0.5,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0.5,0,0,0.5]
recall
0.34375 [0.5,0,1,0,1,0,0,1,0,0,1,1,0.5,1,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0.5,0,1,0.5,0,1,0,1,0.5,0,0.5,0.5,0,0,0,0.5,0,0,0,0.5,0.5,0,1,1,0,0.5,0,0,0,1,0.5,0.5,0,0,0,1,0,0.5,1,0,1,0.5,0,0,1,0,0,0,1,1,0,1,0,0.5,0,0,0,1,1,0,0,0,0,1,0,0.5,0,0,0.5,0.5,0,0,0.5]
recall
0.34375 [0.5,1,1,0,1,0,0.5,1,0,0.5,0,1,0,0,0,0,1,0,0,0,0,0.5,0,1,0.5,0,0,0.5,1,0,0,1,1,1,0,1,0.5,0,0,1,0,0.5,1,0,1,0,1,1,0,0,0,0,0.5,1,0,0,1,0.5,0,0,0,0.5,1,0,0.5,0,0,0.5,0,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0,1,1,0,0,0,1,0.5,0,0,0]
recall
0.4 [0,1,1,0,0,1,0.5,0,0,1,0,1,1,0,1,0,1,0,1,0,0.5,1,1,1,0.5,0,0,1,0.5,1,0,1,0,0,1,1,0.5,0,0,1,0,0.5,0,0.5,0,0,1,0,0,1,0.5,0,1,0,0,0,0.5,0,0,0,0,0,0.5,0,1,1,0.5,0,0.5,0,0,0,0.5,0,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,0,1,0,0.5,0,1,0.5,0,0.5,0,1]
recall
0.41875 [0,0,0,0,1,0,0,0,0.5,0,0,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0.5,0,0,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,0,1,0,0,0.5,0.5,1,0,1,0.5,1,0,0.5,0,1,0,0.5,0,1,1,0,1,1,0,0.5,0,1,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0]
relative_absolute_error
0.6906049051961154
relative_absolute_error
0.7128742428034297
relative_absolute_error
0.7100880469973022
relative_absolute_error
0.717690803487028
relative_absolute_error
0.7248292413738423
relative_absolute_error
0.7237371056619589
relative_absolute_error
0.7453259303500162
relative_absolute_error
0.7292194774344657
relative_absolute_error
0.7009576847364771
relative_absolute_error
0.6895805532977185
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
0.08854982508618944
root_mean_squared_error
0.08859495410441938
root_mean_squared_error
0.09062604817865297
root_mean_squared_error
0.09019031282847483
root_mean_squared_error
0.09338631254016623
root_mean_squared_error
0.09129439121505682
root_mean_squared_error
0.0927908586398614
root_mean_squared_error
0.09153850851775133
root_mean_squared_error
0.08800578759330552
root_mean_squared_error
0.08774131268148537
root_relative_squared_error
0.8899592274620914
root_relative_squared_error
0.8904127911609575
root_relative_squared_error
0.9108260546704942
root_relative_squared_error
0.9064467496267516
root_relative_squared_error
0.9385677553048256
root_relative_squared_error
0.9175431549220044
root_relative_squared_error
0.9325832184343262
root_relative_squared_error
0.9199966261276751
root_relative_squared_error
0.88449144492942
root_relative_squared_error
0.8818333720537539
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
151.92138300153601
usercpu_time_millis
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usercpu_time_millis_testing
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usercpu_time_millis_training
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