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Recursive feature elimination#

This example demonstrates how Recursive Feature Elimination (RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least significant features, assigning ranks based on their importance, where higher ranking_ values denote lower importance. The ranking is visualized using both shades of blue and pixel annotations for clarity. As expected, pixels positioned at the center of the image tend to be more predictive than those near the edges.

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Ranking of pixels with RFE (Logistic Regression)

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
importmatplotlib.pyplotasplt
fromsklearn.datasetsimport load_digits
fromsklearn.feature_selectionimport RFE
fromsklearn.linear_modelimport LogisticRegression
fromsklearn.pipelineimport Pipeline
fromsklearn.preprocessingimport MinMaxScaler
# Load the digits dataset
digits = load_digits ()
X = digits.images.reshape((len(digits.images), -1))
y = digits.target
pipe = Pipeline (
 [
 ("scaler", MinMaxScaler ()),
 ("rfe", RFE (estimator=LogisticRegression (), n_features_to_select=1, step=1)),
 ]
)
pipe.fit(X, y)
ranking = pipe.named_steps["rfe"].ranking_.reshape(digits.images[0].shape)
# Plot pixel ranking
plt.matshow (ranking, cmap=plt.cm.Blues)
# Add annotations for pixel numbers
for i in range(ranking.shape[0]):
 for j in range(ranking.shape[1]):
 plt.text (j, i, str(ranking[i, j]), ha="center", va="center", color="black")
plt.colorbar ()
plt.title ("Ranking of pixels with RFE\n(Logistic Regression)")
plt.show ()