make_union (original) (raw)

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 Glossaryfor 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())])