Preface

The title of this book suggests its central themes: interpretation, machine learning, and Python, with the first theme being the most crucial.

So, why is interpretation so important?

Interpretable machine learning, often referred to as Explainable AI (XAI), encompasses a growing array of techniques that help us glean insights from models, aiming to ensure they are safe, fair, and reliable – a goal I believe we all share for our models.

With the rise of AI superseding traditional software and even human tasks, machine learning models are viewed as a more advanced form of software. While they operate on binary data, they aren’t typical software; their logic isn’t explicitly coded by developers but emerges from data patterns. This is where interpretation steps in, helping us understand these models, pinpoint their errors, and rectify them before any potential mishaps. Thus, interpretation is essential in fostering trust and ethical considerations in these models. And it’s worth noting that in the not-so-distant future, training models might move away from coding to more intuitive drag-and-drop interfaces. In this context, understanding machine learning models becomes an invaluable skill.

Currently, there’s still a significant amount of coding involved in data preprocessing, exploration, model training, and deployment. And while this book is rich with Python examples, it’s not merely a coding guide removed from practical applications or the bigger picture. The book’s essence is to prioritize the why before the how when it comes to interpretable machine learning, as interpretation revolves around the question of why.

Therefore, most chapters of this book kickoff by outlining a mission (the why) and then delving into the methodology (the how). The aim is to achieve the mission using the techniques discussed in the chapter, with an emphasis on understanding the results. The chapters wrap up by pondering on the practical insights gained from the exercises.

The structure of this book is progressive, starting from the basics and moving to more intricate topics. The tools utilized in this book are open source and are products of leading research institutions like Microsoft, Google, and IBM. Even though interpretability is a vast research field with many aspects still in the developmental phase, this book doesn’t aim to cover it all. Its primary goal is to delve deeply into a selection of interpretability tools, making it beneficial for those working in the machine learning domain.

The book’s initial section introduces interpretability, emphasizing its significance in the business landscape and discussing its core components and challenges. The subsequent section provides a detailed overview of various interpretation techniques and their applications, whether it’s for classification, regression, tabular data, time series, images, or text. In the final section, readers will engage in practical exercises on model tuning and data training for interpretability, focusing on simplifying models, addressing biases, setting constraints, and ensuring dependability.

By the book’s conclusion, readers will be adept at using interpretability techniques to gain deeper insights into machine learning models.