| 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 | 0.026961164924839395 |
| sklearn.naive_bayes.MultinomialNB(6)_class_prior | null |
| sklearn.naive_bayes.MultinomialNB(6)_fit_prior | true |
| 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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0.7932 ± 0.0174 Per class Cross-validation details (10-fold Crossvalidation)
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0.0776 ± 0.0391 Cross-validation details (10-fold Crossvalidation)
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-3.3449 ± 0.2774 Cross-validation details (10-fold Crossvalidation)
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0.2852 ± 0.0201 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 ± 0.0247 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 ± 0.0247 Cross-validation details (10-fold Crossvalidation)
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0.3029 ± 0.0027 Cross-validation details (10-fold Crossvalidation)
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2.7905 ± 0.1997 Cross-validation details (10-fold Crossvalidation)
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0.2259 ± 0.0013 Cross-validation details (10-fold Crossvalidation)
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0.5059 ± 0.0211 Cross-validation details (10-fold Crossvalidation)
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2.2394 ± 0.0962 Cross-validation details (10-fold Crossvalidation)
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0.617 ± 0.0573 Cross-validation details (10-fold Crossvalidation)
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