| Issue | #Downvotes for this reason | By |
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| sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imput ation.ImputationComponent,step_1=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.naive_bayes.BernoulliNB(11)_alpha | 74.0088732079853 |
| sklearn.naive_bayes.BernoulliNB(11)_binarize | 0.0 |
| sklearn.naive_bayes.BernoulliNB(11)_class_prior | null |
| sklearn.naive_bayes.BernoulliNB(11)_fit_prior | true |
| automl.components.data_preprocessing.imputation.ImputationComponent(1)_add_indicator | true |
| automl.components.data_preprocessing.imputation.ImputationComponent(1)_missing_values | NaN |
| automl.components.data_preprocessing.imputation.ImputationComponent(1)_strategy | "most_frequent" |
| sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.naive_bayes.BernoulliNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,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.data_preprocessing.imputation.ImputationComponent,step_1=sklearn.naive_bayes.BernoulliNB)(1)_verbose | false |
0.979 ± 0.006 Per class Cross-validation details (10-fold Crossvalidation)
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0.8336 ± 0.0337 Per class Cross-validation details (10-fold Crossvalidation)
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0.8083 ± 0.0387 Cross-validation details (10-fold Crossvalidation)
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0.5671 ± 0.0202 Cross-validation details (10-fold Crossvalidation)
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0.121 ± 0.0039 Cross-validation details (10-fold Crossvalidation)
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0.1975 Cross-validation details (10-fold Crossvalidation)
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0.8296 ± 0.0344 Cross-validation details (10-fold Crossvalidation)
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1080 Per class Cross-validation details (10-fold Crossvalidation) |
0.8604 ± 0.0325 Per class Cross-validation details (10-fold Crossvalidation)
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0.8296 ± 0.0344 Cross-validation details (10-fold Crossvalidation)
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3.1699 Cross-validation details (10-fold Crossvalidation)
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0.6127 ± 0.0197 Cross-validation details (10-fold Crossvalidation)
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0.3143 Cross-validation details (10-fold Crossvalidation)
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0.2155 ± 0.0057 Cross-validation details (10-fold Crossvalidation)
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0.6858 ± 0.0181 Cross-validation details (10-fold Crossvalidation)
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0.8296 ± 0.0344 Cross-validation details (10-fold Crossvalidation)
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