make_union#

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

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())])