Run
9662706

Run 9662706

Task 9983 (Supervised Classification) eeg-eye-state 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.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C93.63683197138718
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sklearn.svm.classes.SVC(22)_class_weightnull
sklearn.svm.classes.SVC(22)_coef00.8481284605695918
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sklearn.svm.classes.SVC(22)_degree3
sklearn.svm.classes.SVC(22)_gamma3.504064054510293e-05
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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
0.5037 ± 0.0071
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4021 ± 0.0228
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0082 ± 0.0155
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1417.676 ± 17.1984
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4459 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.619 ± 0.0815
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5541 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924
Cross-validation details (10-fold Crossvalidation)
Recall
0.5541 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9012 ± 0.0114
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6677 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.3425 ± 0.0086
Cross-validation details (10-fold Crossvalidation)