| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.No rmalizer,step_2=sklearn.naive_bayes.BernoulliNB)(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 | 65.89733346211675 |
| 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.preprocessing._data.Normalizer(1)_copy | false |
| sklearn.preprocessing._data.Normalizer(1)_norm | "l2" |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Normalizer,step_2=sklearn.naive_bayes.BernoulliNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Normalizer,step_2=sklearn.naive_bayes.BernoulliNB)(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.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Normalizer,step_2=sklearn.naive_bayes.BernoulliNB)(1)_verbose | false |
0.6472 ± 0.0416 Per class Cross-validation details (10-fold Crossvalidation)
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0.8278 ± 0.0104 Per class Cross-validation details (10-fold Crossvalidation)
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0.0173 ± 0.0539 Cross-validation details (10-fold Crossvalidation)
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-0.1514 ± 0.089 Cross-validation details (10-fold Crossvalidation)
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0.1757 ± 0.0092 Cross-validation details (10-fold Crossvalidation)
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0.2041 ± 0.0005 Cross-validation details (10-fold Crossvalidation)
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0.8655 ± 0.0098 Cross-validation details (10-fold Crossvalidation)
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4521 Per class Cross-validation details (10-fold Crossvalidation) |
0.802 ± 0.0206 Per class Cross-validation details (10-fold Crossvalidation)
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0.8655 ± 0.0098 Cross-validation details (10-fold Crossvalidation)
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0.5155 ± 0.0018 Cross-validation details (10-fold Crossvalidation)
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0.861 ± 0.0444 Cross-validation details (10-fold Crossvalidation)
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0.3193 ± 0.0007 Cross-validation details (10-fold Crossvalidation)
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0.3297 ± 0.0127 Cross-validation details (10-fold Crossvalidation)
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1.0327 ± 0.0395 Cross-validation details (10-fold Crossvalidation)
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0.5058 ± 0.0179 Cross-validation details (10-fold Crossvalidation)
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