| Issue | #Downvotes for this reason | By |
|---|
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_sele ction.SelectPercentile,step_1=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.naive_bayes.MultinomialNB(6)_alpha | 0.1135368082945935 |
| sklearn.naive_bayes.MultinomialNB(6)_class_prior | null |
| sklearn.naive_bayes.MultinomialNB(6)_fit_prior | false |
| sklearn.feature_selection._univariate_selection.SelectPercentile(1)_percentile | 22.333941173119946 |
| 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=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.naive_bayes.MultinomialNB)(1)_memory | null |
| sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.naive_bayes.MultinomialNB)(1)_verbose | false |
0.5531 ± 0.1153 Per class Cross-validation details (10-fold Crossvalidation)
|
0.5 ± 0.127 Per class Cross-validation details (10-fold Crossvalidation)
|
0.0747 ± 0.0946 Cross-validation details (10-fold Crossvalidation)
|
-0.003 ± 0.0387 Cross-validation details (10-fold Crossvalidation)
|
0.4956 ± 0.0143 Cross-validation details (10-fold Crossvalidation)
|
0.4948 ± 0 Cross-validation details (10-fold Crossvalidation)
|
0.5184 ± 0.0693 Cross-validation details (10-fold Crossvalidation)
|
14980 Per class Cross-validation details (10-fold Crossvalidation) |
0.5534 ± 0.0807 Per class Cross-validation details (10-fold Crossvalidation)
|
0.5184 ± 0.0693 Cross-validation details (10-fold Crossvalidation)
|
0.9924 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
|
1.0018 ± 0.0288 Cross-validation details (10-fold Crossvalidation)
|
0.4974 ± 0 Cross-validation details (10-fold Crossvalidation)
|
0.5111 ± 0.0124 Cross-validation details (10-fold Crossvalidation)
|
1.0275 ± 0.0248 Cross-validation details (10-fold Crossvalidation)
|
0.5393 ± 0.0481 Cross-validation details (10-fold Crossvalidation)
|