Lecture
This lecture covers the concept of variable selection in machine learning, focusing on subset selection and container methods. Subset selection involves determining the best subset of variables for a learning algorithm, while container methods use algorithms to select variables. The instructor explains the naive method, which exhaustively tries all combinations of variables, and non-exhaustive methods like forward and backward search. The lecture also introduces floating search, a greedy procedure for selecting variables. Examples and workout scenarios are provided to illustrate the different selection approaches.