Decision Trees and CLT's: Inference and Machine Learning

This lecture by the instructor covers decision trees, ensembles of trees, and the central limit theorem in the context of inference and machine learning. It delves into the importance of understanding prediction functions, the use of tree-based ensembles like bagging and random forests, and the challenges of extrapolation and feature importance assessment. The lecture also explores diagnostic methods in machine learning, including variable importance and partial dependence functions. Furthermore, it discusses the application of ensemble methods in statistical questions, boosting techniques, and variance estimation in random forests. The lecture concludes with insights on boosting in structured models and future directions in statistical inference and deep learning.