Interpretable Machine Learning

Interpretable machine learning (IML) is rapidly gaining popularity in the data science community. It offers a new way to build and interpret models that are more transparent and understandable. In this episode, we have the privilege of interviewing Serg Masis. Serg authored the book "Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples".

You'll see, that this concept not only applies to very complex models but even to simple regression models with several factors. 

He walks us through the concept of interpretability and explains why it is better than explainability. He also discusses black-box and white-box models. Additionally, he introduces us to glass box models and explores various topics and modeling approaches related to IML that you don't want to miss.

We also, discuss the following points: