| Issue | #Downvotes for this reason | By |
|---|
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._uni variate_selection.SelectPercentile,step_2=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 | 68.72859050251735 |
| sklearn.naive_bayes.BernoulliNB(11)_binarize | 0.0 |
| sklearn.naive_bayes.BernoulliNB(11)_class_prior | null |
| sklearn.naive_bayes.BernoulliNB(11)_fit_prior | false |
| sklearn.feature_selection._univariate_selection.SelectPercentile(1)_percentile | 57.44382793396026 |
| sklearn.feature_selection._univariate_selection.SelectPercentile(1)_score_func | {"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._mutual_info.mutual_info_classif"} |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,step_2=sklearn.naive_bayes.BernoulliNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}] |
| sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,step_2=sklearn.naive_bayes.BernoulliNB)(1)_verbose | false |
0.6442 ± 0.0477 Per class Cross-validation details (10-fold Crossvalidation)
|
0.7969 ± 0.021 Per class Cross-validation details (10-fold Crossvalidation)
|
0.1625 ± 0.0754 Cross-validation details (10-fold Crossvalidation)
|
-1.921 ± 0.0805 Cross-validation details (10-fold Crossvalidation)
|
0.368 ± 0.0129 Cross-validation details (10-fold Crossvalidation)
|
0.2041 ± 0.0005 Cross-validation details (10-fold Crossvalidation)
|
0.7706 ± 0.0258 Cross-validation details (10-fold Crossvalidation)
|
4521 Per class Cross-validation details (10-fold Crossvalidation) |
0.8333 ± 0.0168 Per class Cross-validation details (10-fold Crossvalidation)
|
0.7706 ± 0.0258 Cross-validation details (10-fold Crossvalidation)
|
0.5155 ± 0.0018 Cross-validation details (10-fold Crossvalidation)
|
1.8033 ± 0.0621 Cross-validation details (10-fold Crossvalidation)
|
0.3193 ± 0.0007 Cross-validation details (10-fold Crossvalidation)
|
0.4406 ± 0.0174 Cross-validation details (10-fold Crossvalidation)
|
1.3799 ± 0.0537 Cross-validation details (10-fold Crossvalidation)
|
0.6091 ± 0.0484 Cross-validation details (10-fold Crossvalidation)
|