Run
9683095

Run 9683095

Task 9954 (Supervised Classification) one-hundred-plants-margin Uploaded 20-10-2018 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.data.StandardScaler(14)_with_meantrue
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sklearn.svm.classes.SVC(22)_gamma0.28112379198421183
sklearn.svm.classes.SVC(22)_kernel"rbf"
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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"
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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
0.6894 ± 0.0147
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4871
Per class
Kappa
0.3788 ± 0.0294
Cross-validation details (10-fold Crossvalidation)
KB relative information score
613.8525 ± 4.6816
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0123 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0198
Cross-validation details (10-fold Crossvalidation)
Number of instances
1600
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7776
Per class
Predictive accuracy
0.385 ± 0.0292
Cross-validation details (10-fold Crossvalidation)
Prior entropy
6.6439
Cross-validation details (10-fold Crossvalidation)
Recall
0.385 ± 0.0292
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6212 ± 0.0295
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.0995
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1109 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1146 ± 0.0264
Cross-validation details (10-fold Crossvalidation)