Lecture
This lecture covers various methods for variable selection in statistical modeling, including automatic selection algorithms, information criteria, and prediction error analysis. It discusses the trade-off between bias and variance, model diagnostics, and the Akaike information criterion. The instructor emphasizes the importance of selecting the right model complexity to improve prediction accuracy.