Run
9766396

Run 9766396

Task 14964 (Supervised Classification) artificial-characters Uploaded 21-10-2018 by Scikit-learn Bot
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Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.pr eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder )),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT hreshold,svc=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
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sklearn.preprocessing.imputation.Imputer(29)_copytrue
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sklearn.preprocessing.data.StandardScaler(14)_with_meantrue
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sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
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sklearn.svm.classes.SVC(22)_C30561.263362263366
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sklearn.svm.classes.SVC(22)_coef00.13473819267407716
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sklearn.svm.classes.SVC(22)_gamma1.2560039033867945
sklearn.svm.classes.SVC(22)_kernel"sigmoid"
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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))(1)_remainder"passthrough"
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))(1)_sparse_threshold0.3
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
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Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

17 Evaluation measures

Area under ROC curve
0.5023 ± 0.004
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.1018 ± 0.0066
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0043 ± 0.0081
Cross-validation details (10-fold Crossvalidation)
KB relative information score
658.7881 ± 7.7916
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1776 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.179 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
10218
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.1126 ± 0.0158
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.112 ± 0.0068
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.2849
Cross-validation details (10-fold Crossvalidation)
Recall
0.112 ± 0.0068
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9922 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2992 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4214 ± 0.0016
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.4087 ± 0.0054
Cross-validation details (10-fold Crossvalidation)