.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_svm_plot_svm_anova.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_svm_plot_svm_anova.py:


=================================================
SVM-Anova: SVM with univariate feature selection
=================================================

This example shows how to perform univariate feature selection before running a
SVC (support vector classifier) to improve the classification scores. We use
the iris dataset (4 features) and add 36 non-informative features. We can find
that our model achieves best performance when we select around 10% of features.



.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_anova_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none







|


.. code-block:: default

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.datasets import load_iris
    from sklearn.feature_selection import SelectPercentile, chi2
    from sklearn.model_selection import cross_val_score
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.svm import SVC


    # #############################################################################
    # Import some data to play with
    X, y = load_iris(return_X_y=True)
    # Add non-informative features
    np.random.seed(0)
    X = np.hstack((X, 2 * np.random.random((X.shape[0], 36))))

    # #############################################################################
    # Create a feature-selection transform, a scaler and an instance of SVM that we
    # combine together to have an full-blown estimator
    clf = Pipeline([('anova', SelectPercentile(chi2)),
                    ('scaler', StandardScaler()),
                    ('svc', SVC(gamma="auto"))])

    # #############################################################################
    # Plot the cross-validation score as a function of percentile of features
    score_means = list()
    score_stds = list()
    percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)

    for percentile in percentiles:
        clf.set_params(anova__percentile=percentile)
        this_scores = cross_val_score(clf, X, y, cv=5)
        score_means.append(this_scores.mean())
        score_stds.append(this_scores.std())

    plt.errorbar(percentiles, score_means, np.array(score_stds))
    plt.title(
        'Performance of the SVM-Anova varying the percentile of features selected')
    plt.xticks(np.linspace(0, 100, 11, endpoint=True))
    plt.xlabel('Percentile')
    plt.ylabel('Accuracy Score')
    plt.axis('tight')
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.312 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_svm_anova.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_svm_anova.py <plot_svm_anova.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_svm_anova.ipynb <plot_svm_anova.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_