.. _sphx_glr_auto_examples_feature_selection_plot_feature_selection.py:


===============================
Univariate Feature Selection
===============================

An example showing univariate feature selection.

Noisy (non informative) features are added to the iris data and
univariate feature selection is applied. For each feature, we plot the
p-values for the univariate feature selection and the corresponding
weights of an SVM. We can see that univariate feature selection
selects the informative features and that these have larger SVM weights.

In the total set of features, only the 4 first ones are significant. We
can see that they have the highest score with univariate feature
selection. The SVM assigns a large weight to one of these features, but also
Selects many of the non-informative features.
Applying univariate feature selection before the SVM
increases the SVM weight attributed to the significant features, and will
thus improve classification.




.. image:: /auto_examples/feature_selection/images/sphx_glr_plot_feature_selection_001.png
    :align: center





.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn import datasets, svm
    from sklearn.feature_selection import SelectPercentile, f_classif

    # #############################################################################
    # Import some data to play with

    # The iris dataset
    iris = datasets.load_iris()

    # Some noisy data not correlated
    E = np.random.uniform(0, 0.1, size=(len(iris.data), 20))

    # Add the noisy data to the informative features
    X = np.hstack((iris.data, E))
    y = iris.target

    plt.figure(1)
    plt.clf()

    X_indices = np.arange(X.shape[-1])

    # #############################################################################
    # Univariate feature selection with F-test for feature scoring
    # We use the default selection function: the 10% most significant features
    selector = SelectPercentile(f_classif, percentile=10)
    selector.fit(X, y)
    scores = -np.log10(selector.pvalues_)
    scores /= scores.max()
    plt.bar(X_indices - .45, scores, width=.2,
            label=r'Univariate score ($-Log(p_{value})$)', color='darkorange',
            edgecolor='black')

    # #############################################################################
    # Compare to the weights of an SVM
    clf = svm.SVC(kernel='linear')
    clf.fit(X, y)

    svm_weights = (clf.coef_ ** 2).sum(axis=0)
    svm_weights /= svm_weights.max()

    plt.bar(X_indices - .25, svm_weights, width=.2, label='SVM weight',
            color='navy', edgecolor='black')

    clf_selected = svm.SVC(kernel='linear')
    clf_selected.fit(selector.transform(X), y)

    svm_weights_selected = (clf_selected.coef_ ** 2).sum(axis=0)
    svm_weights_selected /= svm_weights_selected.max()

    plt.bar(X_indices[selector.get_support()] - .05, svm_weights_selected,
            width=.2, label='SVM weights after selection', color='c',
            edgecolor='black')


    plt.title("Comparing feature selection")
    plt.xlabel('Feature number')
    plt.yticks(())
    plt.axis('tight')
    plt.legend(loc='upper right')
    plt.show()

**Total running time of the script:** ( 0 minutes  0.066 seconds)



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



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