Run
10560513

Run 10560513

Task 10093 (Supervised Classification) banknote-authentication Uploaded 14-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(nystroem=sklearn.kernel_approximation.Nystroem,fe atureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,gaussi annb=sklearn.naive_bayes.GaussianNB)(2)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 to None.
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_affinity"euclidean"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_compute_full_tree"auto"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_connectivitynull
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_linkage"average"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_memorynull
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_n_clusters2
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.mean"}
sklearn.pipeline.Pipeline(nystroem=sklearn.kernel_approximation.Nystroem,featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,gaussiannb=sklearn.naive_bayes.GaussianNB)(2)_memorynull
sklearn.pipeline.Pipeline(nystroem=sklearn.kernel_approximation.Nystroem,featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,gaussiannb=sklearn.naive_bayes.GaussianNB)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "nystroem", "step_name": "nystroem"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "featureagglomeration", "step_name": "featureagglomeration"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gaussiannb", "step_name": "gaussiannb"}}]
sklearn.kernel_approximation.Nystroem(4)_coef0null
sklearn.kernel_approximation.Nystroem(4)_degreenull
sklearn.kernel_approximation.Nystroem(4)_gamma0.15000000000000002
sklearn.kernel_approximation.Nystroem(4)_kernel"laplacian"
sklearn.kernel_approximation.Nystroem(4)_kernel_paramsnull
sklearn.kernel_approximation.Nystroem(4)_n_components9
sklearn.kernel_approximation.Nystroem(4)_random_state40147
sklearn.naive_bayes.GaussianNB(21)_priorsnull

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
0.7049 ± 0.0771
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6521 ± 0.0432
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3176 ± 0.0849
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2219 ± 0.0825
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3885 ± 0.0404
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6531 ± 0.0427
Cross-validation details (10-fold Crossvalidation)
Number of instances
1372
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6742 ± 0.0464
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6531 ± 0.0427
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9911 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7866 ± 0.0817
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4969 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4657 ± 0.0266
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9372 ± 0.0532
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6635 ± 0.0439
Cross-validation details (10-fold Crossvalidation)