Run
10461741

Run 10461741

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.cl uster._agglomerative.FeatureAgglomeration,step_2=sklearn.discriminant_analy sis.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.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components48
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)_tol0.0005628114875883886
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columnsnull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity"manhattan"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_compute_full_treefalse
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_connectivitynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_distance_thresholdnull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_linkage"average"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memorynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters5
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.amax"}
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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.cluster._agglomerative.FeatureAgglomeration,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.cluster._agglomerative.FeatureAgglomeration,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
0.8208 ± 0.0383
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.872 ± 0.0149
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2996 ± 0.0924
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1006 ± 0.0622
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1495 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2041 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8898 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Number of instances
4521
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8678 ± 0.0174
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8898 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5155 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7327 ± 0.0354
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
0.2969 ± 0.0158
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9298 ± 0.0484
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6156 ± 0.0417
Cross-validation details (10-fold Crossvalidation)