Run
9797643

Run 9797643

Task 3510 (Supervised Classification) JapaneseVowels Uploaded 22-10-2018 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
sklearn.preprocessing.imputation.Imputer(29)_missing_values"NaN"
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sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C53.67252550004625
sklearn.svm.classes.SVC(22)_cache_size200
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sklearn.svm.classes.SVC(22)_coef00.9816227080724236
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sklearn.svm.classes.SVC(22)_degree3
sklearn.svm.classes.SVC(22)_gamma0.0037790640780714113
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
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)_transformer_weightsnull
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
0.9508 ± 0.0052
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9124 ± 0.0095
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9011 ± 0.0107
Cross-validation details (10-fold Crossvalidation)
KB relative information score
9023.8898 ± 10.1556
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0194 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1962 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
9961
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9125 ± 0.0093
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9127 ± 0.0094
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1321
Cross-validation details (10-fold Crossvalidation)
Recall
0.9127 ± 0.0094
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0989 ± 0.0107
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3132 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1393 ± 0.0077
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4448 ± 0.0246
Cross-validation details (10-fold Crossvalidation)