Interpretable Machine Learning with Python

A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models

Key Features

• Learn how to extract easy-to-understand insights from any machine learning model
• Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
• Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models

Book Description

What you will learn

• Recognize the importance of interpretability in business
• Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes
• Become well-versed in interpreting models with model-agnostic methods
• Visualize how an image classifier works and what it learns
• Understand how to mitigate the influence of bias in datasets
• Discover how to make models more reliable with adversarial robustness
• Use monotonic constraints to make fairer and safer models

Who this book is for

This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.