Book Image

Interpretable Machine Learning with Python

By : Serg Masís
Book Image

Interpretable Machine Learning with Python

By: Serg Masís

Overview of this book

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 deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you 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, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using 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. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
Table of Contents (19 chapters)
Section 1: Introduction to Machine Learning Interpretation
Section 2: Mastering Interpretation Methods
Section 3:Tuning for Interpretability

Mission accomplished

It's often the data that takes the blame for a poor-performing, uninterpretable, or biased model, and that can be true, but many different things can be done in the preparation and modeling stages to improve it. To offer an analogy, it's like baking a cake. You need quality ingredients, yes. But seemingly small differences in the preparation of these ingredients and baking itself—such as the baking temperature, the container used, and time—can make a huge difference. Hell! Even things that are out of your control, such as atmospheric pressure or moisture, can impact baking! Even after it's all finished, how many different ways can you assess the quality of a cake?

This chapter is about these many details, and, as with baking, they are part exact science and part artform. The concepts discussed in this chapter also have far-reaching consequences, especially regarding how to optimize a problem that doesn't have a single goal and...