| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.mu lti_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.na ive_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 | 122.73729863759704 |
| sklearn.naive_bayes.BernoulliNB(11)_binarize | 0.0 |
| sklearn.naive_bayes.BernoulliNB(11)_class_prior | null |
| sklearn.naive_bayes.BernoulliNB(11)_fit_prior | false |
| automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent(1)_columns | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.naive_bayes.BernoulliNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=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"}}] |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.naive_bayes.BernoulliNB)(1)_verbose | false |
0.633 ± 0.0414 Per class Cross-validation details (10-fold Crossvalidation)
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0.8049 ± 0.0151 Per class Cross-validation details (10-fold Crossvalidation)
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0.1475 ± 0.0598 Cross-validation details (10-fold Crossvalidation)
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-0.9239 ± 0.0785 Cross-validation details (10-fold Crossvalidation)
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0.2831 ± 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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0.7866 ± 0.0178 Cross-validation details (10-fold Crossvalidation)
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4521 Per class Cross-validation details (10-fold Crossvalidation) |
0.828 ± 0.013 Per class Cross-validation details (10-fold Crossvalidation)
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0.7866 ± 0.0178 Cross-validation details (10-fold Crossvalidation)
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0.5155 ± 0.0018 Cross-validation details (10-fold Crossvalidation)
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1.3872 ± 0.0585 Cross-validation details (10-fold Crossvalidation)
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0.3193 ± 0.0007 Cross-validation details (10-fold Crossvalidation)
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0.3904 ± 0.019 Cross-validation details (10-fold Crossvalidation)
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1.2227 ± 0.059 Cross-validation details (10-fold Crossvalidation)
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0.5906 ± 0.0359 Cross-validation details (10-fold Crossvalidation)
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