Run
10461520

Run 10461520

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._forest.RandomForest Classifier),step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis )(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._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.33971976150852723
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features5
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes4442
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.6083968161393437
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.02550218537633009
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.12236858928453045
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.23033830860954638
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.1375860269368115
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1831
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components240
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)_tol0.005704911833530854
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._forest.RandomForestClassifier),step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(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._forest.RandomForestClassifier),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=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),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

Area under ROC curve
0.8453 ± 0.0263
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8386 ± 0.0072
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0535 ± 0.0457
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2733 ± 0.0253
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1144 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8861 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
Number of instances
4521
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8548 ± 0.0357
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8861 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.5606 ± 0.0165
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
0.3282 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0278 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5158 ± 0.014
Cross-validation details (10-fold Crossvalidation)