What does L1 regularization encourage?

Question: What is a common effect of L1 regularization? A) It always increases all feature weights B) It prevents feature selection C) It encourages sparse coefficients by pushing some weights exactly to zero D) It removes the loss function Correct: C Explanation: L1 regularization adds a penalty proportional to the absolute value of coefficients. This can push some coefficients to exactly zero, making it useful for sparse models and feature selection. Topic: advanced ML / regularization / sparsity