Run
10460182

Run 10460182

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=sklearn.pr eprocessing._data.PolynomialFeatures,step_2=sklearn.discriminant_analysis.L inearDiscriminantAnalysis)(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.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components236
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkagenull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"svd"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.0002571706507925155
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.preprocessing._data.PolynomialFeatures(1)_degree2
sklearn.preprocessing._data.PolynomialFeatures(1)_include_biasfalse
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_onlyfalse
sklearn.preprocessing._data.PolynomialFeatures(1)_order"C"
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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

0.8615 ± 0.0305
Per class
Cross-validation details (10-fold Crossvalidation)
0.8836 ± 0.0174
Per class
Cross-validation details (10-fold Crossvalidation)
0.3988 ± 0.095
Cross-validation details (10-fold Crossvalidation)
0.1766 ± 0.0656
Cross-validation details (10-fold Crossvalidation)
0.132 ± 0.0101
Cross-validation details (10-fold Crossvalidation)
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
0.8903 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
4521
Per class
Cross-validation details (10-fold Crossvalidation)
0.8793 ± 0.0195
Per class
Cross-validation details (10-fold Crossvalidation)
0.8903 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
0.6471 ± 0.0494
Cross-validation details (10-fold Crossvalidation)
0.3193 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.293 ± 0.0199
Cross-validation details (10-fold Crossvalidation)
0.9176 ± 0.062
Cross-validation details (10-fold Crossvalidation)
0.6784 ± 0.0464
Cross-validation details (10-fold Crossvalidation)