| Issue | #Downvotes for this reason | By |
|---|
| TEST1aa56ebf63sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.Simple Imputer,regressor=sklearn.linear_model._base.LinearRegression)(1) | 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... |
| TEST1aa56ebf63sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_memory | null |
| TEST1aa56ebf63sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "regressor", "step_name": "regressor"}}] |
| TEST1aa56ebf63sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_verbose | false |
| TEST1aa56ebf63sklearn.impute._base.SimpleImputer(1)_add_indicator | false |
| TEST1aa56ebf63sklearn.impute._base.SimpleImputer(1)_copy | true |
| TEST1aa56ebf63sklearn.impute._base.SimpleImputer(1)_fill_value | null |
| TEST1aa56ebf63sklearn.impute._base.SimpleImputer(1)_keep_empty_features | false |
| TEST1aa56ebf63sklearn.impute._base.SimpleImputer(1)_missing_values | NaN |
| TEST1aa56ebf63sklearn.impute._base.SimpleImputer(1)_strategy | "mean" |
| TEST1aa56ebf63sklearn.linear_model._base.LinearRegression(1)_copy_X | true |
| TEST1aa56ebf63sklearn.linear_model._base.LinearRegression(1)_fit_intercept | true |
| TEST1aa56ebf63sklearn.linear_model._base.LinearRegression(1)_n_jobs | null |
| TEST1aa56ebf63sklearn.linear_model._base.LinearRegression(1)_positive | false |
0.1486 ± 0.0063 |
0.1491 ± 0.0067 |
2178 |
0.9962 ± 0.0102 |
0.1894 ± 0.0107 |
0.189 ± 0.0103 |
0.9981 ± 0.0047 |