Lecture
This lecture focuses on the concept of overfitting in supervised learning, particularly through the lens of polynomial regression. It begins with a case study that assumes a true model represented by a 10th-order polynomial, where noise is introduced. The instructor demonstrates how fitting both 2nd and 10th order polynomials to a limited dataset of 15 points can lead to unexpected results, emphasizing that a more complex model does not always yield better out-of-sample performance. The discussion extends to model selection techniques, including subset selection and regularization methods, which aim to balance model complexity and predictive accuracy. The lecture also covers performance metrics such as R², mean square error, and cross-validation methods, highlighting their importance in evaluating model effectiveness. Finally, the instructor presents practical applications, including case studies on predicting used car prices, illustrating how different models can be assessed and selected based on their predictive capabilities.