sklearn.pipeline.make_union(*transformers, **kwargs)[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.

*transformerslist of estimators
n_jobsint or None, optional (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.

verboseboolean, optional(default=False)

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


See also


Class for concatenating the results of multiple transformer objects.


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

Examples using sklearn.pipeline.make_union