.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/compose/plot_feature_union.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_compose_plot_feature_union.py: ================================================= Concatenating multiple feature extraction methods ================================================= In many real-world examples, there are many ways to extract features from a dataset. Often it is beneficial to combine several methods to obtain good performance. This example shows how to use ``FeatureUnion`` to combine features obtained by PCA and univariate selection. Combining features using this transformer has the benefit that it allows cross validation and grid searches over the whole process. The combination used in this example is not particularly helpful on this dataset and is only used to illustrate the usage of FeatureUnion. .. GENERATED FROM PYTHON SOURCE LINES 18-63 .. rst-class:: sphx-glr-script-out .. code-block:: none Combined space has 3 features Fitting 5 folds for each of 18 candidates, totalling 90 fits [CV 1/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 [CV 1/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s [CV 2/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 [CV 2/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s [CV 3/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 [CV 3/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.867 total time= 0.0s [CV 4/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 [CV 4/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s [CV 5/5; 1/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1 [CV 5/5; 1/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s [CV 1/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1 [CV 1/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=0.900 total time= 0.0s [CV 2/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1 [CV 2/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s [CV 3/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1 [CV 3/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=0.867 total time= 0.0s [CV 4/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1 [CV 4/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s [CV 5/5; 2/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=1 [CV 5/5; 2/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s [CV 1/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10 [CV 1/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s [CV 2/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10 [CV 2/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s [CV 3/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10 [CV 3/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=0.900 total time= 0.0s [CV 4/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10 [CV 4/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s [CV 5/5; 3/18] START features__pca__n_components=1, features__univ_select__k=1, svm__C=10 [CV 5/5; 3/18] END features__pca__n_components=1, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s [CV 1/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 [CV 1/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s [CV 2/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 [CV 2/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s [CV 3/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 [CV 3/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s [CV 4/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 [CV 4/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s [CV 5/5; 4/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1 [CV 5/5; 4/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s [CV 1/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1 [CV 1/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s [CV 2/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1 [CV 2/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s [CV 3/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1 [CV 3/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s [CV 4/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1 [CV 4/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s [CV 5/5; 5/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=1 [CV 5/5; 5/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s [CV 1/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10 [CV 1/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s [CV 2/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10 [CV 2/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s [CV 3/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10 [CV 3/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.933 total time= 0.0s [CV 4/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10 [CV 4/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=0.933 total time= 0.0s [CV 5/5; 6/18] START features__pca__n_components=1, features__univ_select__k=2, svm__C=10 [CV 5/5; 6/18] END features__pca__n_components=1, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s [CV 1/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 [CV 1/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s [CV 2/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 [CV 2/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s [CV 3/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 [CV 3/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=0.867 total time= 0.0s [CV 4/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 [CV 4/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s [CV 5/5; 7/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1 [CV 5/5; 7/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s [CV 1/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1 [CV 1/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=0.967 total time= 0.0s [CV 2/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1 [CV 2/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s [CV 3/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1 [CV 3/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s [CV 4/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1 [CV 4/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s [CV 5/5; 8/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=1 [CV 5/5; 8/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s [CV 1/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10 [CV 1/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.967 total time= 0.0s [CV 2/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10 [CV 2/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.967 total time= 0.0s [CV 3/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10 [CV 3/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.900 total time= 0.0s [CV 4/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10 [CV 4/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s [CV 5/5; 9/18] START features__pca__n_components=2, features__univ_select__k=1, svm__C=10 [CV 5/5; 9/18] END features__pca__n_components=2, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s [CV 1/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 [CV 1/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s [CV 2/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 [CV 2/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s [CV 3/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 [CV 3/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s [CV 4/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 [CV 4/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s [CV 5/5; 10/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1 [CV 5/5; 10/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s [CV 1/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1 [CV 1/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s [CV 2/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1 [CV 2/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s [CV 3/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1 [CV 3/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=0.933 total time= 0.0s [CV 4/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1 [CV 4/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s [CV 5/5; 11/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=1 [CV 5/5; 11/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s [CV 1/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10 [CV 1/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s [CV 2/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10 [CV 2/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s [CV 3/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10 [CV 3/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=0.900 total time= 0.0s [CV 4/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10 [CV 4/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=0.933 total time= 0.0s [CV 5/5; 12/18] START features__pca__n_components=2, features__univ_select__k=2, svm__C=10 [CV 5/5; 12/18] END features__pca__n_components=2, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s [CV 1/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 [CV 1/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=0.967 total time= 0.0s [CV 2/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 [CV 2/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s [CV 3/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 [CV 3/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=0.933 total time= 0.0s [CV 4/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 [CV 4/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=0.967 total time= 0.0s [CV 5/5; 13/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1 [CV 5/5; 13/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1;, score=1.000 total time= 0.0s [CV 1/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1 [CV 1/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=0.967 total time= 0.0s [CV 2/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1 [CV 2/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s [CV 3/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1 [CV 3/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=0.933 total time= 0.0s [CV 4/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1 [CV 4/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=0.967 total time= 0.0s [CV 5/5; 14/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=1 [CV 5/5; 14/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=1;, score=1.000 total time= 0.0s [CV 1/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10 [CV 1/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s [CV 2/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10 [CV 2/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s [CV 3/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10 [CV 3/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=0.933 total time= 0.0s [CV 4/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10 [CV 4/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=0.967 total time= 0.0s [CV 5/5; 15/18] START features__pca__n_components=3, features__univ_select__k=1, svm__C=10 [CV 5/5; 15/18] END features__pca__n_components=3, features__univ_select__k=1, svm__C=10;, score=1.000 total time= 0.0s [CV 1/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 [CV 1/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s [CV 2/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 [CV 2/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s [CV 3/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 [CV 3/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=0.933 total time= 0.0s [CV 4/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 [CV 4/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=0.967 total time= 0.0s [CV 5/5; 16/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1 [CV 5/5; 16/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1;, score=1.000 total time= 0.0s [CV 1/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1 [CV 1/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s [CV 2/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1 [CV 2/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s [CV 3/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1 [CV 3/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s [CV 4/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1 [CV 4/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=0.967 total time= 0.0s [CV 5/5; 17/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=1 [CV 5/5; 17/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=1;, score=1.000 total time= 0.0s [CV 1/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10 [CV 1/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s [CV 2/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10 [CV 2/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s [CV 3/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10 [CV 3/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=0.900 total time= 0.0s [CV 4/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10 [CV 4/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=0.967 total time= 0.0s [CV 5/5; 18/18] START features__pca__n_components=3, features__univ_select__k=2, svm__C=10 [CV 5/5; 18/18] END features__pca__n_components=3, features__univ_select__k=2, svm__C=10;, score=1.000 total time= 0.0s Pipeline(steps=[('features', FeatureUnion(transformer_list=[('pca', PCA(n_components=3)), ('univ_select', SelectKBest(k=1))])), ('svm', SVC(C=10, kernel='linear'))]) | .. code-block:: Python # Author: Andreas Mueller # # License: BSD 3 clause from sklearn.datasets import load_iris from sklearn.decomposition import PCA from sklearn.feature_selection import SelectKBest from sklearn.model_selection import GridSearchCV from sklearn.pipeline import FeatureUnion, Pipeline from sklearn.svm import SVC iris = load_iris() X, y = iris.data, iris.target # This dataset is way too high-dimensional. Better do PCA: pca = PCA(n_components=2) # Maybe some original features were good, too? selection = SelectKBest(k=1) # Build estimator from PCA and Univariate selection: combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)]) # Use combined features to transform dataset: X_features = combined_features.fit(X, y).transform(X) print("Combined space has", X_features.shape[1], "features") svm = SVC(kernel="linear") # Do grid search over k, n_components and C: pipeline = Pipeline([("features", combined_features), ("svm", svm)]) param_grid = dict( features__pca__n_components=[1, 2, 3], features__univ_select__k=[1, 2], svm__C=[0.1, 1, 10], ) grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10) grid_search.fit(X, y) print(grid_search.best_estimator_) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.368 seconds) .. _sphx_glr_download_auto_examples_compose_plot_feature_union.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/compose/plot_feature_union.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/compose/plot_feature_union.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_feature_union.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_feature_union.py ` .. include:: plot_feature_union.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_