SVM-Anova: SVM with univariate feature selection (original) (raw)
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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#
Create the pipeline#
Plot the cross-validation score as a function of percentile of features#
import matplotlib.pyplot as plt
from sklearn.model_selection import 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()
Total running time of the script: (0 minutes 0.399 seconds)
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