Interpretability Techniques

Model interpretability refers to how easy it is to understand how a model works. Common techniques include SHAP, LIME, PDPs and more.

A visual representation of model interpretability techniques in machine learning. Model interpretability is the ability to determine how an algorithm arrived at its conclusions.

Table of ContentsDifferentiating Interpretability TypesExploring Core Interpretability Techniques

This particular example perfectly highlights why Interpretability Techniques is so captivating.

This paper explores various interpretability techniques in ML, categorized into intrinsic and post-hoc methods.

Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them and the rich structure of this combinatorial space.

Interpretability Techniques | 21. Figure 9: Global random forest and local LOCO feature importance.

2.8 Interpretability techniques. Practical guidance cross-domain. Authors: Rhys Ward. One way to provide assurance is to make the ML system being used interpretable.