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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/svm/plot_svm_anova.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_svm_plot_svm_anova.py>`
        to download the full example code or to run this example in your browser via Binder

.. 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.

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Load some data to play with
---------------------------

.. GENERATED FROM PYTHON SOURCE LINES 16-26

.. code-block:: default

    import numpy as np

    from sklearn.datasets import load_iris

    X, y = load_iris(return_X_y=True)

    # Add non-informative features
    rng = np.random.RandomState(0)
    X = np.hstack((X, 2 * rng.random((X.shape[0], 36))))








.. GENERATED FROM PYTHON SOURCE LINES 27-29

Create the pipeline
-------------------

.. GENERATED FROM PYTHON SOURCE LINES 29-45

.. code-block:: default

    from sklearn.feature_selection import SelectPercentile, f_classif
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.svm import SVC

    # Create a feature-selection transform, a scaler and an instance of SVM that we
    # combine together to have a full-blown estimator

    clf = Pipeline(
        [
            ("anova", SelectPercentile(f_classif)),
            ("scaler", StandardScaler()),
            ("svc", SVC(gamma="auto")),
        ]
    )








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Plot the cross-validation score as a function of percentile of features
-----------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 48-69

.. code-block:: default

    import matplotlib.pyplot as plt

    from sklearn.model_selection import cross_val_score

    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)
        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()



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_svm_anova_001.png
   :alt: Performance of the SVM-Anova varying the percentile of features selected
   :srcset: /auto_examples/svm/images/sphx_glr_plot_svm_anova_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.194 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_svm_anova.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/1.3.X?urlpath=lab/tree/notebooks/auto_examples/svm/plot_svm_anova.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: sphx-glr-download sphx-glr-download-python

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

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


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