Run
1853406

Run 1853406

Task 29 (Supervised Classification) credit-approval Uploaded 21-03-2017 by Jan van Rijn
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  • openml-python Sklearn_0.18. study_14
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Flow

sklearn.pipeline.Pipeline(Imputer=openml.utils.preprocessing.ConditionalImp uter,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,VarianceThresho ld=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator =sklearn.naive_bayes.GaussianNB)(1)Automatically created scikit-learn flow.
sklearn.naive_bayes.GaussianNB(2)_priorsNone
sklearn.pipeline.Pipeline(Imputer=openml.utils.preprocessing.ConditionalImputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(1)_steps[('Imputer', ConditionalImputer(axis=0, categorical_features=[0, 3, 4, 5, 6, 8, 9, 11, 12], copy=True, empty_attribute_constant=0, missing_values='NaN', strategy='median', strategy_nominal='most_frequent', verbose=0)), ('OneHotEncoder', OneHotEncoder(categorical_features=[0, 3, 4, 5, 6, 8, 9, 11, 12], dtype=, handle_unknown='ignore', n_values='auto', sparse=False)), ('VarianceThreshold', VarianceThreshold(threshold=0.0)), ('Estimator', GaussianNB(priors=None))]
openml.utils.preprocessing.ConditionalImputer(1)_axis0
openml.utils.preprocessing.ConditionalImputer(1)_categorical_features[0, 3, 4, 5, 6, 8, 9, 11, 12]
openml.utils.preprocessing.ConditionalImputer(1)_copyTrue
openml.utils.preprocessing.ConditionalImputer(1)_empty_attribute_constant0
openml.utils.preprocessing.ConditionalImputer(1)_missing_valuesNaN
openml.utils.preprocessing.ConditionalImputer(1)_strategymedian
openml.utils.preprocessing.ConditionalImputer(1)_strategy_nominalmost_frequent
openml.utils.preprocessing.ConditionalImputer(1)_verbose0
sklearn.preprocessing.data.OneHotEncoder(4)_categorical_features[0, 3, 4, 5, 6, 8, 9, 11, 12]
sklearn.preprocessing.data.OneHotEncoder(4)_dtype
sklearn.preprocessing.data.OneHotEncoder(4)_handle_unknownignore
sklearn.preprocessing.data.OneHotEncoder(4)_n_valuesauto
sklearn.preprocessing.data.OneHotEncoder(4)_sparseFalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.0

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
0.9164
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.829
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6528
Cross-validation details (10-fold Crossvalidation)
KB relative information score
459.2423
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.164
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.494
Cross-validation details (10-fold Crossvalidation)
Number of instances
690
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.832
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8304
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9913
Cross-validation details (10-fold Crossvalidation)
Recall
0.8304
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3321
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.497
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3815
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7676
Cross-validation details (10-fold Crossvalidation)