As machine learning models get into the hands of more and more users, there's an increasing expectation that black box isn't good enough: users want to understand why the model made a given prediction, not just what the prediction itself is. This is motivating a lot of work into feature important and model interpretability tools, and one of the most exciting new ones is based on Shapley Values from game theory. In this episode, we'll explain what Shapley Values are and how they make a cool approach to feature importance for machine learning.
Relevant links:
- A previous Linear Digressions episode on LIME, another feature importance algorithm
- A Unified Approach to Interpreting Model Predictions
- Game Theory Attribution: The Model You've Probably Never Heard Of (good introductory explanation to Shapley values)
- One Feature Attribution Method to (Supposedly) Rule Them All: Shapley Values