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

Placing guardrails with feature engineering

In Chapter 7, Anchors and Counterfactual Explanations, we learned that besides race, the features most prominent in our explanations were age, priors_count, and c_charge_degree. Thankfully, the data is now balanced, so the racial bias attributed to this imbalance is now gone. However, through anchor and counterfactual explanations, we found some troubling inconsistencies. In the case of age and priors_count, these inconsistencies were due to how those features were distributed. We can correct issues with distribution through feature engineering, and that way ensure that a model doesn't learn from uneven distributions. In c_charge_degree's case being categorical, it lacked a discernible order, and this lack of order created unintuitive explanations.

In this section, we will study ordinalization, discretization, and interaction terms, three ways in which you can place guardrails through feature engineering.