How do you use L1 regularization with Logistic Regression in Scikit-learn for feature selection

Question

Can i know How do you use L1 regularization with Logistic Regression in Scikit-learn for feature selection?

Answer 1

You can use L1 regularization with LogisticRegression in Scikit-learn to perform feature selection by shrinking some feature coefficients to zero.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• penalty='l1' applies L1 regularization, which encourages sparsity in the feature weights.
• solver='liblinear' supports L1 regularization for logistic regression.
• np.where(model.coef_[0] != 0) identifies non-zero coefficients, indicating selected features.
• accuracy_score() evaluates model performance after feature selection.

Hence, L1 regularization helps select the most important features by reducing irrelevant feature weights to zero, improving model interpretability and efficiency.