Linear Model Selection and Regularization (Part 2)

Abstract: In the second session, we turn to Regularization Methods, focusing on ridge regression and the lasso. We'll examine how these techniques use penalty terms to control model flexibility, reduce overfitting, and enhance prediction accuracy, with hands-on Python examples illustrating their practical differences and applications.

Computer scienceMathematics

Audience: general audience

Basic Notions and Applied Topology Seminar