| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(esti mator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.naive_bayes.Multinomi alNB)(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.naive_bayes.BernoulliNB(11)_alpha | 20.71488944796319 |
| sklearn.naive_bayes.BernoulliNB(11)_binarize | 0.0 |
| sklearn.naive_bayes.BernoulliNB(11)_class_prior | null |
| sklearn.naive_bayes.BernoulliNB(11)_fit_prior | false |
| sklearn.naive_bayes.MultinomialNB(6)_alpha | 0.0072474722402687914 |
| sklearn.naive_bayes.MultinomialNB(6)_class_prior | null |
| sklearn.naive_bayes.MultinomialNB(6)_fit_prior | true |
| sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.naive_bayes.MultinomialNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.naive_bayes.MultinomialNB)(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"}}] |
| sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=sklearn.naive_bayes.MultinomialNB)(1)_verbose | false |
0.9952 ± 0.0038 Per class Cross-validation details (10-fold Crossvalidation)
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0.951 ± 0.0218 Per class Cross-validation details (10-fold Crossvalidation)
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0.9448 ± 0.0246 Cross-validation details (10-fold Crossvalidation)
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0.9523 ± 0.0213 Cross-validation details (10-fold Crossvalidation)
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0.0113 ± 0.0047 Cross-validation details (10-fold Crossvalidation)
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0.1975 Cross-validation details (10-fold Crossvalidation)
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0.9509 ± 0.0218 Cross-validation details (10-fold Crossvalidation)
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1080 Per class Cross-validation details (10-fold Crossvalidation) |
0.9518 ± 0.0198 Per class Cross-validation details (10-fold Crossvalidation)
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0.9509 ± 0.0218 Cross-validation details (10-fold Crossvalidation)
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3.1699 Cross-validation details (10-fold Crossvalidation)
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0.057 ± 0.024 Cross-validation details (10-fold Crossvalidation)
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0.3143 Cross-validation details (10-fold Crossvalidation)
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0.093 ± 0.0205 Cross-validation details (10-fold Crossvalidation)
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0.2959 ± 0.0652 Cross-validation details (10-fold Crossvalidation)
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0.9509 ± 0.0218 Cross-validation details (10-fold Crossvalidation)
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