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TESTa47fb41514sklearn.pipeline.FeatureUnion(pca=sklearn.decomposition._truncated_svd.TruncatedSVD,fs=sklearn.feature_selection._univariate_selection.SelectPercentile)

TESTa47fb41514sklearn.pipeline.FeatureUnion(pca=sklearn.decomposition._truncated_svd.TruncatedSVD,fs=sklearn.feature_selection._univariate_selection.SelectPercentile)

Visibility: public Uploaded 10-01-2024 by Continuous Integration sklearn==0.23.1 numpy>=1.13.3 scipy>=0.19.1 joblib>=0.11 threadpoolctl>=2.0.0 0 runs
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Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. A transformer may be replaced entirely by setting the parameter with its name to another transformer, or removed by setting to 'drop'.

Parameters

n_jobsNumber of jobs to run in parallel ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context ``-1`` means using all processors. See :term:`Glossary ` for more details .. versionchanged:: v0.20 `n_jobs` default changed from 1 to Nonedefault: null
transformer_listList of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer .. versionchanged:: 0.22 Deprecated `None` as a transformer in favor of 'drop'default: [{"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "fs", "step_name": "fs"}}]
transformer_weightsMultiplicative weights for features per transformer Keys are transformer names, values the weightsdefault: null
verboseIf True, the time elapsed while fitting each transformer will be printed as it is completed See Also --------default: false

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