Model Selection: Least Squares

This lecture covers model selection techniques in the context of least squares regression. It discusses methods like backward elimination and bidirectional elimination, as well as prediction error-based criteria and information criteria. The lecture also delves into the challenges posed by multicollinearity and explores remedies such as ridge regression. Various strategies for diagnosing and addressing multicollinearity are presented, along with the concept of shrinkage in regression models.