| Issue | #Downvotes for this reason | By |
|---|
| sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Varian ceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold ,Estimator=sklearn.naive_bayes.GaussianNB)(4) | A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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`. For an example use case of `Pipeline` combined with :class:`~s... |
| sklearn.impute._base.SimpleImputer(3)_add_indicator | false |
| sklearn.impute._base.SimpleImputer(3)_copy | true |
| sklearn.impute._base.SimpleImputer(3)_fill_value | null |
| sklearn.impute._base.SimpleImputer(3)_keep_empty_features | false |
| sklearn.impute._base.SimpleImputer(3)_missing_values | NaN |
| sklearn.impute._base.SimpleImputer(3)_strategy | "most_frequent" |
| sklearn.feature_selection._variance_threshold.VarianceThreshold(3)_threshold | 0.05 |
| sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(4)_memory | null |
| sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(4)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}] |
| sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(4)_verbose | false |
| sklearn.naive_bayes.GaussianNB(4)_priors | null |
| sklearn.naive_bayes.GaussianNB(4)_var_smoothing | 1e-09 |
0.9944 ± 0.0064 Per class |
0.96 ± 0.048 Per class |
0.94 ± 0.0699 |
0.93 ± 0.0568 |
0.0365 ± 0.027 |
0.4444 |
0.96 ± 0.0466 |
150 Per class |
0.96 ± 0.0355 Per class |
0.96 ± 0.0466 |
1.585 |
0.0822 ± 0.0608 |
0.4714 |
0.1538 ± 0.0914 |
0.3262 ± 0.1939 |
0.96 ± 0.0466 |