Interpretable Machine Learning: Sparse Decision Trees and Interpretable Neural Networks

This lecture by the instructor covers the extremes of interpretability in machine learning, focusing on sparse decision trees and interpretable neural networks. The presentation delves into the technical definition of interpretable machine learning models, the problem spectrum of tabular and raw data, the comparison between COMPAS and CORELS models, and the prediction accuracy of re-arrest within 2 years. It also explores the optimal sparse decision trees construction, dynamic programming for branch and bound, and the Generalized and Scalable Optimal Sparse Decision Trees (GOSDT). Additionally, the lecture discusses interpretable neural networks, the challenges of explaining deep neural networks, and the development of an interpretable AI algorithm for breast lesions. The session concludes with a summary of modern decision tree methods and the existence of interpretable neural networks for computer vision.