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

The approach

The bank has stressed to you how important it is that there's fairness embedded in your methods because the regulators and the public at large want assurance that banks will not cause any more harm. Their reputation depends on it too, because in the past months, the media has been relentless in blaming them for dishonest and predatory lending practices, causing distrust in consumers. For this reason, they want to use state-of-the-art robustness testing to demonstrate that the prescribed policies will alleviate the problem. Your proposed approach includes the following points:

  • Younger lenders have been reported to be more prone to default on repayment, so you expect to find age bias, but you will also look for bias with other protected groups such as gender.
  • Once you have detected bias, you can mitigate bias with preprocessing, in-processing, and post-processing algorithms using the AI Fairness 360 (AIF360) library. In this process, you will train different...