Recursive feature elimination

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

Note

../../_images/sphx_glr_plot_rfe_digits_001.png

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

Links:

http://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html

doc_scikit_learn

2025-01-10 15:47:30

Comments