IDL Lect 3C Interpretable Models Before Deep Learning | Feature Importance, Naïve Bayes & Dec. Trees

In this lecture, Prof. Dilip K. Prasad explains how classical machine learning models provided interpretable explanations before deep learning became dominant. The lecture introduces non-graphical explanation methods such as feature importance, probability-based explanations, and decision rules. It discusses why models such as Naïve Bayes, Random Forests, and decision trees are easier to inspect compared with many deep neural networks, and how their structure can help users understand predictions. The lecture also explains important limitations, including why feature importance does not necessarily mean causality and why complex models may require additional explanation tools. It connects classical interpretable models with modern Explainable AI approaches, including surrogate models and other XAI strategies. This lecture is part of a course series on interpretable and explainable artificial intelligence.