| 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.St andardScaler,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.preprocessing._data.StandardScaler(1)_copy | false |
| sklearn.preprocessing._data.StandardScaler(1)_with_mean | false |
| sklearn.preprocessing._data.StandardScaler(1)_with_std | true |
| sklearn.naive_bayes.BernoulliNB(11)_alpha | 114.29522198900884 |
| 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.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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.StandardScaler,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.StandardScaler,step_2=sklearn.naive_bayes.BernoulliNB)(1)_verbose | false |
0.6351 ± 0.0412 Per class Cross-validation details (10-fold Crossvalidation)
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0.8039 ± 0.0157 Per class Cross-validation details (10-fold Crossvalidation)
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0.1474 ± 0.0614 Cross-validation details (10-fold Crossvalidation)
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-0.9879 ± 0.0763 Cross-validation details (10-fold Crossvalidation)
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0.2889 ± 0.0121 Cross-validation details (10-fold Crossvalidation)
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0.2041 ± 0.0005 Cross-validation details (10-fold Crossvalidation)
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0.7848 ± 0.0187 Cross-validation details (10-fold Crossvalidation)
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4521 Per class Cross-validation details (10-fold Crossvalidation) |
0.8281 ± 0.0132 Per class Cross-validation details (10-fold Crossvalidation)
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0.7848 ± 0.0187 Cross-validation details (10-fold Crossvalidation)
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0.5155 ± 0.0018 Cross-validation details (10-fold Crossvalidation)
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1.416 ± 0.0581 Cross-validation details (10-fold Crossvalidation)
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0.3193 ± 0.0007 Cross-validation details (10-fold Crossvalidation)
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0.3937 ± 0.0189 Cross-validation details (10-fold Crossvalidation)
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1.2329 ± 0.0587 Cross-validation details (10-fold Crossvalidation)
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0.5912 ± 0.0368 Cross-validation details (10-fold Crossvalidation)
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