Bounded Rationality (Posts about game theory)http://bjlkeng.github.io/enSun, 29 Mar 2020 14:00:11 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rssModel Explainability with SHapley Additive exPlanations (SHAP)http://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 moreā¦</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