Run
9850257

Run 9850257

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
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sklearn.preprocessing.data.StandardScaler(14)_with_stdtrue
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sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C0.270552399777145
sklearn.svm.classes.SVC(22)_cache_size200
sklearn.svm.classes.SVC(22)_class_weightnull
sklearn.svm.classes.SVC(22)_coef0-0.1976848066888348
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sklearn.svm.classes.SVC(22)_degree4
sklearn.svm.classes.SVC(22)_gamma0.2284178492661499
sklearn.svm.classes.SVC(22)_kernel"poly"
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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
0.6317 ± 0.0063
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3627 ± 0.0116
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2674 ± 0.0126
Cross-validation details (10-fold Crossvalidation)
KB relative information score
3188.6741 ± 11.4152
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1424 ± 0.0024
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
0.4091 ± 0.0171
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.3593 ± 0.0107
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1321
Cross-validation details (10-fold Crossvalidation)
Recall
0.3593 ± 0.0107
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7258 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3132 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3773 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2048 ± 0.0101
Cross-validation details (10-fold Crossvalidation)