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make_union — scikit-learn 1.7.0 documentation

sklearn.pipeline.make_union(*transformers, n_jobs=None, verbose=False, verbose_feature_names_out=True)[source]#

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:

*transformerslist of estimators

One or more estimators.

n_jobsint, 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.

verbosebool, default=False

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

verbose_feature_names_outbool, default=True

If True, the feature names generated by get_feature_names_out will include prefixes derived from the transformer names.

Returns:

fFeatureUnion

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())])

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