.. 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: ================================================= دمج طرق استخراج ميزات متعددة ================================================= في العديد من الأمثلة الواقعية، هناك العديد من الطرق لاستخراج الميزات من مجموعة بيانات. غالبًا ما يكون من المفيد الجمع بين عدة طرق للحصول على أداء جيد. يوضح هذا المثال كيفية استخدام ``FeatureUnion`` لدمج الميزات التي تم الحصول عليها بواسطة PCA والاختيار أحادي المتغير. يُتيح دمج الميزات باستخدام هذا المحول ميزة أنه يسمح بالتحقق المتبادل والبحث الشبكي خلال العملية بأكملها. إن التركيبة المستخدمة في هذا المثال ليست مفيدة بشكل خاص في مجموعة البيانات هذه ولا تُستخدم إلا لتوضيح استخدام FeatureUnion. .. GENERATED FROM PYTHON SOURCE LINES 13-57 .. 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 # Authors: The scikit-learn developers # SPDX-License-Identifier: 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 # مجموعة البيانات هذه عالية الأبعاد للغاية. من الأفضل القيام بـ PCA: pca = PCA(n_components=2) # ربما كانت بعض الميزات الأصلية جيدة أيضًا؟ selection = SelectKBest(k=1) # بناء مقدر من PCA والاختيار أحادي المتغير: combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)]) # استخدام الميزات المدمجة لتحويل مجموعة البيانات: X_features = combined_features.fit(X, y).transform(X) print("Combined space has", X_features.shape[1], "features") svm = SVC(kernel="linear") # إجراء بحث شبكي على k و n_components و 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 0.481 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/index.html?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 ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_feature_union.zip ` .. include:: plot_feature_union.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_