http://bjlkeng.github.io/enTue, 10 Mar 2026 20:54:58 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rsshttp://bjlkeng.github.io/posts/model-explanability-with-shapley-additive-explanations-shap/Brian Keng<div><p>One of the big criticisms of modern machine learning is that it's essentially a blackbox -- data in, prediction out, that's it. And in some sense, how could it be any other way? When you have a highly non-linear model with high degrees of interactions, how can you possibly hope to have a simple understanding of what the model is doing? Well, turns out there is an interesting (and practical) line of research along these lines.</p> <p>This post will dive into the ideas of a popular technique published in the last few years call <em>SHapely Additive exPlanations</em> (or SHAP). It builds upon previous work in this area by providing a unified framework to think about explanation models as well as a new technique with this framework that uses Shapely values. I'll go over the math, the intuition, and how it works. No need for an implementation because there is already a nice little Python package! Confused yet? Keep reading and I'll <em>explain</em>.</p> <p><a href="http://bjlkeng.github.io/posts/model-explanability-with-shapley-additive-explanations-shap/">Read more…</a> (26 min remaining to read)</p></div>explainabilitygame theorymathjaxSHAPhttp://bjlkeng.github.io/posts/model-explanability-with-shapley-additive-explanations-shap/Wed, 12 Feb 2020 11:24:22 GMT