

.. _sphx_glr_auto_examples_feature_selection_feature_selection_pipeline.py:


==================
Pipeline Anova SVM
==================

Simple usage of Pipeline that runs successively a univariate
feature selection with anova and then a C-SVM of the selected features.


.. code-block:: python

    print(__doc__)

    from sklearn import svm
    from sklearn.datasets import samples_generator
    from sklearn.feature_selection import SelectKBest, f_regression
    from sklearn.pipeline import make_pipeline

    # import some data to play with
    X, y = samples_generator.make_classification(
        n_features=20, n_informative=3, n_redundant=0, n_classes=4,
        n_clusters_per_class=2)

    # ANOVA SVM-C
    # 1) anova filter, take 3 best ranked features
    anova_filter = SelectKBest(f_regression, k=3)
    # 2) svm
    clf = svm.SVC(kernel='linear')

    anova_svm = make_pipeline(anova_filter, clf)
    anova_svm.fit(X, y)
    anova_svm.predict(X)

**Total running time of the script:**
(0 minutes 0.000 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`feature_selection_pipeline.py <feature_selection_pipeline.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`feature_selection_pipeline.ipynb <feature_selection_pipeline.ipynb>`
