Run
10460215

Run 10460215

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.de composition._factor_analysis.FactorAnalysis,step_2=sklearn.ensemble._weight _boosting.AdaBoostClassifier)(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_rate0.06235557435391279
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators940
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_copyfalse
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_iterated_power3
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_max_iter54
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_n_components1
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_noise_variance_initnull
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_random_state42
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_svd_method"lapack"
sklearn.decomposition._factor_analysis.FactorAnalysis(1)_tol0.8078445564574096
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.decomposition._factor_analysis.FactorAnalysis,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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
0.5701 ± 0.0499
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8303 ± 0.0016
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0013 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-3.1174 ± 0.0157
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4929 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8841 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Number of instances
4521
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7827 ± 0.002
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8841 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
2.4158 ± 0.0055
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
0.493 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.5439 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4996 ± 0.0006
Cross-validation details (10-fold Crossvalidation)