sklearn.pipeline.make_union — scikit-learn 0.20.4 documentation (original) (raw)
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.
| 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 Glossaryfor more details. |
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| Returns: | f : FeatureUnion |
Examples
from sklearn.decomposition import PCA, TruncatedSVD from sklearn.pipeline import make_union make_union(PCA(), TruncatedSVD())
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)