“The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as a black box and demand interpretation. Yimou Li, David Turkington, and Alireza Yazdani present a framework for demystifying the behavior of machine learning models. They decompose model predictions into linear, nonlinear, and interaction components and study a model’s predictive efficacy using the same components. Together, this forms a fingerprint to summarize key characteristics, similarities, and differences among different models.”