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.
import some data to play with
Univariate feature selection with F-test for feature scoring We use the default selection function: the 10% most significant features
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') 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') 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 0.225 seconds)