| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.naive_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.impute._base.SimpleImputer(10)_add_indicator | false |
| sklearn.impute._base.SimpleImputer(10)_copy | true |
| sklearn.impute._base.SimpleImputer(10)_fill_value | null |
| sklearn.impute._base.SimpleImputer(10)_missing_values | NaN |
| sklearn.impute._base.SimpleImputer(10)_strategy | "mean" |
| sklearn.impute._base.SimpleImputer(10)_verbose | 0 |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.MultinomialNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.MultinomialNB)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}] |
| sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.MultinomialNB)(1)_verbose | false |
| sklearn.naive_bayes.MultinomialNB(7)_alpha | 1.0 |
| sklearn.naive_bayes.MultinomialNB(7)_class_prior | null |
| sklearn.naive_bayes.MultinomialNB(7)_fit_prior | true |
0.624 ± 0.0549 Per class Cross-validation details (10-fold Crossvalidation)
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0.6421 ± 0.0293 Per class Cross-validation details (10-fold Crossvalidation)
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0.1558 ± 0.0769 Cross-validation details (10-fold Crossvalidation)
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0.0774 ± 0.0666 Cross-validation details (10-fold Crossvalidation)
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0.3617 ± 0.0253 Cross-validation details (10-fold Crossvalidation)
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0.4202 Cross-validation details (10-fold Crossvalidation)
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0.639 ± 0.0277 Cross-validation details (10-fold Crossvalidation)
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1000 Per class Cross-validation details (10-fold Crossvalidation) |
0.6455 ± 0.0326 Per class Cross-validation details (10-fold Crossvalidation)
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0.639 ± 0.0277 Cross-validation details (10-fold Crossvalidation)
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0.8813 Cross-validation details (10-fold Crossvalidation)
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0.8609 ± 0.0602 Cross-validation details (10-fold Crossvalidation)
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0.4583 Cross-validation details (10-fold Crossvalidation)
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0.5858 ± 0.0243 Cross-validation details (10-fold Crossvalidation)
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1.2784 ± 0.0529 Cross-validation details (10-fold Crossvalidation)
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0.5793 ± 0.0408 Cross-validation details (10-fold Crossvalidation)
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