Regression Trees and Ensemble Methods in Machine Learning

This lecture covers regression trees, focusing on the CART method and its application in predicting used car prices. The instructor explains the process of selecting optimal cutoffs for predictors, emphasizing the importance of minimizing mean square error (MSE) at each split. The lecture illustrates how to build a regression tree using a single predictor and extends the discussion to multiple predictors. The instructor highlights the risks of overfitting and the necessity of using cross-validation to determine when to stop splitting the tree. Additionally, the lecture introduces ensemble methods, including random forests and boosting, which enhance prediction accuracy by combining multiple trees. The instructor discusses the advantages and disadvantages of these methods, particularly their interpretability and robustness against overfitting. The session concludes with a case study on stock return prediction, demonstrating the practical application of these concepts in finance, and emphasizes the challenges of low signal-to-noise ratios in financial data.