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scikit-learn 1.3.2
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# `sklearn.pipeline`.make_union

sklearn.pipeline.make_union(*transformers, n_jobs=None, verbose=False)

  Construct a `FeatureUnion` from the given transformers.

  This is a shorthand for the `FeatureUnion` constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting.

  Parameters:
    *transformers list of estimators

      One or more estimators.

    n_jobs int, default=None

      Number of jobs to run in parallel. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details.

      Changed in version v0.20: `n_jobs` default changed from 1 to None.

    verbose bool, default=False

      If True, the time elapsed while fitting each transformer will be printed as it is completed.

  Returns:
    f FeatureUnion

      A `FeatureUnion` object for concatenating the results of multiple transformer objects.

  See also

  `FeatureUnion`

    Class for concatenating the results of multiple transformer objects.

  Examples

      >>> from sklearn.decomposition import PCA, TruncatedSVD
      >>> from sklearn.pipeline import make_union
      >>> make_union(PCA(), TruncatedSVD())
       FeatureUnion(transformer_list=[('pca', PCA()),
                                     ('truncatedsvd', TruncatedSVD())])