This book is for practitioners looking for an overview of techniques to make machine learning models more interpretable. It’s also valuable for students, teachers, researchers, and anyone …

This book is essential for machine learning practitioners, data scientists, statisticians, and anyone interested in making their machine learning models interpretable.

Mar 20, 2021 · In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 …

Most of the models and methods explained are presented using real data examples which are described in the Data chapter.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models...

You will learn how to describe interpretable machine learning and differentiate between interpretability and explainability, explain and implement regression models in Python, and …

Interpretable machine learning (ML) refers to machine learning methods that are designed to be easily understood due to their simple structure, such as decision trees and linear models. …

Jul 5, 2024 · This article explores the importance of interpretable machine learning models, various techniques to achieve interpretability, and the balance between interpretability and …

In this survey, we provide fundamental principles, as well as 10 technical challenges in the design of inherently interpretable machine learning models. Let us provide some background.

In this work, we provide fundamen-tal principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical …