A recursive feature elimination example showing the relevance of pixels in a digit classification task.
Note
See also Recursive feature elimination with cross-validation
print(__doc__) from sklearn.svm import SVC from sklearn.datasets import load_digits from sklearn.feature_selection import RFE import matplotlib.pyplot as plt # Load the digits dataset digits = load_digits() X = digits.images.reshape((len(digits.images), -1)) y = digits.target # Create the RFE object and rank each pixel svc = SVC(kernel="linear", C=1) rfe = RFE(estimator=svc, n_features_to_select=1, step=1) rfe.fit(X, y) ranking = rfe.ranking_.reshape(digits.images[0].shape) # Plot pixel ranking plt.matshow(ranking, cmap=plt.cm.Blues) plt.colorbar() plt.title("Ranking of pixels with RFE") plt.show()
Total running time of the script: (0 minutes 4.802 seconds)
Download Python source code:
plot_rfe_digits.py
Download IPython notebook:
plot_rfe_digits.ipynb
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