sklearn.pipeline.make_union

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

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

n_jobs : int 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.

Returns:

f : FeatureUnion

See also

sklearn.pipeline.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())    # doctest: +NORMALIZE_WHITESPACE
FeatureUnion(n_jobs=None,
       transformer_list=[('pca',
                          PCA(copy=True, iterated_power='auto',
                              n_components=None, random_state=None,
                              svd_solver='auto', tol=0.0, whiten=False)),
                         ('truncatedsvd',
                          TruncatedSVD(algorithm='randomized',
                          n_components=2, n_iter=5,
                          random_state=None, tol=0.0))],
       transformer_weights=None)

Examples using sklearn.pipeline.make_union

Imputing missing values before building an estimator