| 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.MultinomialNB)(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.MultinomialNB(6)_alpha | 0.0034063068935974447 |
| sklearn.naive_bayes.MultinomialNB(6)_class_prior | null |
| sklearn.naive_bayes.MultinomialNB(6)_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.MultinomialNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.naive_bayes.MultinomialNB)(1)_verbose | false |
0.7016 ± 0.0418 Per class Cross-validation details (10-fold Crossvalidation)
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0.7411 ± 0.0124 Per class Cross-validation details (10-fold Crossvalidation)
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0.0877 ± 0.0254 Cross-validation details (10-fold Crossvalidation)
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-4.4332 ± 0.1864 Cross-validation details (10-fold Crossvalidation)
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0.3571 ± 0.0147 Cross-validation details (10-fold Crossvalidation)
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0.1022 ± 0.0006 Cross-validation details (10-fold Crossvalidation)
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0.6443 ± 0.0163 Cross-validation details (10-fold Crossvalidation)
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4839 Per class Cross-validation details (10-fold Crossvalidation) |
0.9263 ± 0.0091 Per class Cross-validation details (10-fold Crossvalidation)
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0.6443 ± 0.0163 Cross-validation details (10-fold Crossvalidation)
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0.3029 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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3.4938 ± 0.1444 Cross-validation details (10-fold Crossvalidation)
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0.2259 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.5688 ± 0.0146 Cross-validation details (10-fold Crossvalidation)
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2.5181 ± 0.0663 Cross-validation details (10-fold Crossvalidation)
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0.6675 ± 0.0516 Cross-validation details (10-fold Crossvalidation)
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