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

Understanding limitations of traditional model interpretation methods

In a nutshell, traditional interpretation methods only cover surface-level questions about your models such as the following:

  • In aggregate, do they perform well?
  • What changes in hyperparameters may impact predictive performance?
  • What latent patterns can you find between the features and their predictive performance?

These questions are very limiting if you are trying to understand not only whether your model works but why and how?

This gap in understanding can lead to unexpected issues with your model that won't necessarily be immediately apparent. Let's consider that models, once deployed, are not static but dynamic. They face different challenges than they did in the "lab" when you were training them. They may face not only performance issues but issues with bias such as imbalance with underrepresented classes, or security with adversarial attacks. Realizing that...