Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

by Serg Masís

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 Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python, Second Edition is the book for you. You’ll cover the fundamentals of interpretability, its relevance in business, and explore its key aspects and challenges. See how white-box models work, compare them to black-box and glass-box models, and examine their trade-offs. Get up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, tabular data, time-series, images, or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using many examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. You’ll also look under the hood of the latest NLP transformer models using the Language Interpretability Tool. By the end of this book, you'll understand ML models better and enhance them through interpretability tuning. This book is for data scientists, machine learning developers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact 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 good grasp of the Python programming language is needed to implement the examples. (N.B. Additional chapters to be confirmed upon publication

606 pages, Paperback

Published October 31, 2023

About the author

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making — and machine learning interpretation helps bridge this gap more robustly. His book Interpretable Machine Learning with Python was published by UK-based publisher Packt in April, 2021.