| 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 | 86.76836639001414 |
| sklearn.naive_bayes.BernoulliNB(11)_binarize | 0.0 |
| sklearn.naive_bayes.BernoulliNB(11)_class_prior | null |
| sklearn.naive_bayes.BernoulliNB(11)_fit_prior | true |
| sklearn.naive_bayes.MultinomialNB(6)_alpha | 0.0008623567862007817 |
| 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 |
0.7017 ± 0.0417 Per class Cross-validation details (10-fold Crossvalidation)
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0.7932 ± 0.0175 Per class Cross-validation details (10-fold Crossvalidation)
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0.0776 ± 0.0394 Cross-validation details (10-fold Crossvalidation)
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-3.3455 ± 0.2775 Cross-validation details (10-fold Crossvalidation)
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0.2854 ± 0.0202 Cross-validation details (10-fold Crossvalidation)
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0.1022 ± 0.0006 Cross-validation details (10-fold Crossvalidation)
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0.7163 ± 0.0248 Cross-validation details (10-fold Crossvalidation)
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4839 Per class Cross-validation details (10-fold Crossvalidation) |
0.916 ± 0.0088 Per class Cross-validation details (10-fold Crossvalidation)
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0.7163 ± 0.0248 Cross-validation details (10-fold Crossvalidation)
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0.3029 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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2.7916 ± 0.2001 Cross-validation details (10-fold Crossvalidation)
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0.2259 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.506 ± 0.0212 Cross-validation details (10-fold Crossvalidation)
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2.2402 ± 0.0964 Cross-validation details (10-fold Crossvalidation)
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0.617 ± 0.0574 Cross-validation details (10-fold Crossvalidation)
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