Note

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

Recursive feature elimination#

A recursive feature elimination example showing the relevance of pixels in a digit classification task.

Note

import matplotlib.pyplot as plt

from sklearn.datasets import load_digits
from sklearn.feature_selection import RFE
from sklearn.svm import SVC

# 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()