Note

Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder

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.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

Load some data to play with#

importnumpyasnp
fromsklearn.datasetsimport 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))))

Create the pipeline#

fromsklearn.feature_selectionimport SelectPercentile , f_classif
fromsklearn.pipelineimport Pipeline
fromsklearn.preprocessingimport StandardScaler
fromsklearn.svmimport 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")),
 ]
)

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

importmatplotlib.pyplotasplt
fromsklearn.model_selectionimport 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 ()