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TESTe3e93e8e51sklearn.pipeline.FeatureUnion(pca=sklearn.decomposition._truncated_svd.TruncatedSVD,fs=sklearn.feature_selection._univariate_selection.SelectPercentile)

TESTe3e93e8e51sklearn.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==1.3.2 numpy>=1.17.3 scipy>=1.5.0 joblib>=1.1.1 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, removed by setting to 'drop' or disabled by setting to 'passthrough' (features are passed without transformation).

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. The transformer can be 'drop' for it to be ignored or can be 'passthrough' for features to be passed unchanged .. versionadded:: 1.1 Added the option `"passthrough"` .. 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 weights Raises ValueError if key not present in ``transformer_list``default: null
verboseIf True, the time elapsed while fitting each transformer will be printed as it is completed.default: false

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