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

This chapter's mission was to see whether there was unfair bias in predicting whether a particular defendant would recidivate. We demonstrated that the FPR for African American defendants is 1.87 times higher than for Caucasian defendants. This disparity was confirmed with WIT, indicating that the model in question is much more likely to misclassify the positive class on the basis of race. However, this is a global interpretation method, so it doesn't answer our question regarding a specific defendant. Incidentally, in Chapter 11, Bias Mitigation and Causal Inference Methods, we will cover other global interpretation methods for unfairness.

To ascertain whether the model was racially biased toward the defendant in question, we leveraged anchor and counterfactual explanations – they both output race as a primary feature in their explanations. The anchor did it with relatively high precision and coverage, and Counterfactuals Guided by Prototypes...