# Model Explainability with SHapley Additive exPlanations (SHAP)

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.

This post will dive into the ideas of a popular technique published in the last
few years call *SHapely Additive exPlanations* (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 *explain*.