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

Understanding the current landscape of ML interpretability

First, we will provide some context on how the book relates to the main goals of ML interpretability and how practitioners can start applying the methods to achieve those broad goals. Then, we'll discuss what the current areas of growth in research are.

Tying everything together!

As discussed in Chapter 1, Interpretation, Interpretability, and Explainability; and Why Does It All Matter?, there are three main themes when talking about ML interpretability: Fairness, Accountability, and Transparency (FAT), and each of these presents a series of concerns (see Figure 14.1). I think we can all agree these are all desirable properties for a model! Indeed, these concerns all present opportunities for the improvement of Artificial Intelligence (AI) systems. These improvements start by leveraging model interpretation methods to evaluate models, confirm or dispute assumptions, and find problems.

What your aim is will depend...