JSM Tutorial 2020 - Closing
Channel: Cynthia Rudin
Date: 2020-08-01

Last few slides of JSM tutorial on interpretable ML

An "interpretable machine learning" model is constrained in model form to be either useful to someone, or obey structural knowledge of the domain, such as monotonicity, causality, structural (generative) constraints, additivity, or physical constraints that come from domain knowledge. Interpretable machine learning started as far back as the 1970's, but has gained momentum as a subfield only very recently. Some of the most popular forms of interpretable machine learning models are sparse decision trees, scoring systems (sparse linear models with integer coefficients), and case-based reasoning methods (including interpretable neural networks). I will overview (a very very small fraction of the large amount of) recent research in the area of the  problems discussed above.