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)
1
Section 1: Introduction to Machine Learning Interpretation
5
Section 2: Mastering Interpretation Methods
12
Section 3:Tuning for Interpretability

Chapter 10: Feature Selection and Engineering for Interpretability

In the first three chapters, we discussed how complexity hinders machine learning (ML) interpretability. There's a trade-off because you want some complexity to maximize predictive performance, yet not to the extent that you cannot rely on the model to satisfy the tenets of interpretability: fairness, accountability, and transparency. This chapter is the first of four focused on how to tune for interpretability. One of the easiest ways to improve interpretability is through feature selection. It has many benefits, such as faster training and making the model easier to interpret. But if these two reasons don't convince you, perhaps another one will.

A common misunderstanding is that complex models can self-select features and perform well nonetheless, so why even bother to select features? Yes, many model classes have mechanisms that can take care of useless features, but they aren't perfect. And the...