.. _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. .. image:: /auto_examples/svm/images/sphx_glr_plot_svm_anova_001.png :align: center .. code-block:: python print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets, feature_selection from sklearn.model_selection import cross_val_score from sklearn.pipeline import Pipeline # ############################################################################# # Import some data to play with digits = datasets.load_digits() y = digits.target # Throw away data, to be in the curse of dimension settings y = y[:200] X = digits.data[:200] n_samples = len(y) X = X.reshape((n_samples, -1)) # add 200 non-informative features X = np.hstack((X, 2 * np.random.random((n_samples, 200)))) # ############################################################################# # Create a feature-selection transform and an instance of SVM that we # combine together to have an full-blown estimator transform = feature_selection.SelectPercentile(feature_selection.f_classif) clf = Pipeline([('anova', transform), ('svc', svm.SVC(C=1.0))]) # ############################################################################# # 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) # Compute cross-validation score using 1 CPU this_scores = cross_val_score(clf, X, y, n_jobs=1) 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.xlabel('Percentile') plt.ylabel('Prediction rate') plt.axis('tight') plt.show() **Total running time of the script:** ( 0 minutes 0.522 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_svm_anova.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_svm_anova.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_