Model Agnostic Methods for XAI | Global v.s. Local | Permutation v.s. Surrogate Models

Model agnostic method can be used with any model. In Explainable AI (XAI), this means we can use them to interpret models without looking at their interworkings. This gives us a powerful way to interpret and explain complex black-box machine learning models.
We will elaborate on this definition. We will also discuss the taxonomy of model agnostic methods for interpretability. They can be classified as Global vs local methods or Permutations vs Surrogate models. We end by discussing the limitations of model agnostic methods and their benefits over other approaches to interpretability.
🚀 Free Course 🚀
Signup here: mailchi.mp/40909011987b/signup
XAI course: adataodyssey.com/courses/xai-...
SHAP course: adataodyssey.com/courses/shap...
🚀 Companion Article (no-paywall link): 🚀
medium.com/towards-data-scien...
🚀 Useful playlists 🚀
XAI: • Explainable AI (XAI)
SHAP: • SHAP
Algorithm fairness: • Algorithm Fairness
🚀 Get in touch 🚀
Medium: / conorosullyds
Threads: www.threads.net/@conorosullyds
Twitter: / conorosullyds
Website: adataodyssey.com/
🚀 Chapters 🚀
00:00 Introduction
01:37 What are model agnostic methods?
02:48 Global v.s. local methods
04:43 Permutations v.s. surrogate models
05:53 Benefits and limitations

[@adataodyssey]

🚀 Free Course 🚀
Signup here: mailchi.mp/40909011987b/signup
XAI course: adataodyssey.com/courses/xai-with-python/
SHAP course: adataodyssey.com/courses/shap-with-python/