Model Fingerprint Algorithm

Another way to get a better understanding of a machine learning model is to understand how feature values influence model predictions. Feature effects can be decomposed into 3 components (fingerprints):

  • Linear component

  • Non-linear component

  • Pairwise interaction component

Yimou Li, David Turkington, and Alireza Yazdani published a paper in the Journal of Financial Data Science ‘Beyond the Black Box: An Intuitive Approach to Investment Prediction with Machine Learning’ which describes in details the algorithm of extracting linear, non-linear and pairwise feature effects. This module implements the algorithm described in the article.


  • I would like to highlight that this algorithm is one of the tools that our team uses the most! There are 2 classes which inherit from an abstract base class, you only need to instantiate the child classes.

  • This algorithm is also a favourite of multiple award winning hedge funds!


Code implementation demo
Code implementation demo
Code implementation demo


Code example demo

Research Article