Run
9743177

Run 9743177

Task 3510 (Supervised Classification) JapaneseVowels 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._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C69.52314009757961
sklearn.svm.classes.SVC(22)_cache_size200
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sklearn.svm.classes.SVC(22)_coef00.9669261950775458
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sklearn.svm.classes.SVC(22)_degree1
sklearn.svm.classes.SVC(22)_gamma0.5199159338559403
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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
sklearn.feature_selection.variance_threshold.VarianceThreshold(18)_threshold0.0
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,svc=sklearn.svm.classes.SVC)(1)_memorynull

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
0.9765 ± 0.002
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9582 ± 0.0035
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9527 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
KB relative information score
9512.2491 ± 4.107
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0093 ± 0.0008
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
0.9583 ± 0.0036
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9582 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1321
Cross-validation details (10-fold Crossvalidation)
Recall
0.9582 ± 0.0036
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0473 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3132 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0963 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3076 ± 0.0133
Cross-validation details (10-fold Crossvalidation)