SVM-Anova: SVM with univariate feature selectionΒΆ

This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores.


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 =
# Throw away data, to be in the curse of dimension settings
y = y[:200]
X =[: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:
    # Compute cross-validation score using 1 CPU
    this_scores = cross_val_score(clf, X, y, n_jobs=1)

plt.errorbar(percentiles, score_means, np.array(score_stds))

    'Performance of the SVM-Anova varying the percentile of features selected')
plt.ylabel('Prediction rate')


Total running time of the script: (0 minutes 0.644 seconds)

Download Python source code:
Download IPython notebook: plot_svm_anova.ipynb