Join the reading group: https://hudsonthames.org/reading-group/

The first lecture from the Hudson and Thames Reading Group, focuses on a model interpretability algorithm known as the model fingerprint, first published in the Journal of Financial Data Science.

The algorithm decomposes model predictions into linear, nonlinear, and interaction components and studies a model’s predictive efficacy using the same components. Together, this forms a fingerprint to summarize key characteristics, similarities, and differences among different models.