Run
10461651

Run 10461651

Task 9899 (Supervised Classification) bank-marketing Uploaded 20-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.uti l.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.Ada BoostClassifier),step_2=sklearn.naive_bayes.BernoulliNB)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate2.716323315717944
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators939
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
sklearn.naive_bayes.BernoulliNB(11)_alpha76.81463549268226
sklearn.naive_bayes.BernoulliNB(11)_binarize0.0
sklearn.naive_bayes.BernoulliNB(11)_class_priornull
sklearn.naive_bayes.BernoulliNB(11)_fit_priorfalse
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_2=sklearn.naive_bayes.BernoulliNB)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_2=sklearn.naive_bayes.BernoulliNB)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_2=sklearn.naive_bayes.BernoulliNB)(1)_verbosefalse

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.

18 Evaluation measures

Area under ROC curve
0.6444 ± 0.0415
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8022 ± 0.0173
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1497 ± 0.064
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.9641 ± 0.0781
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2854 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7815 ± 0.0212
Cross-validation details (10-fold Crossvalidation)
Number of instances
4521
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.829 ± 0.0137
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7815 ± 0.0212
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.3984 ± 0.0583
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3193 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3886 ± 0.0192
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2169 ± 0.0595
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5944 ± 0.0381
Cross-validation details (10-fold Crossvalidation)