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

Interpreting SHAP summary and dependence plots

SHapley Additive exPlanations (SHAP) is a collection of methods, or explainers, that approximate Shapley values while adhering to its mathematical properties, for the most part. The paper calls these values SHAP values, but SHAP will be used interchangeably with Shapley in this book. However, it must be noted that the authors of SHAP took a few liberties with the properties. For instance, some explainers don't comply with the dummy property and leverage reference background data to simulate missing values. Despite these issues, because of SHAP being grounded in other solid properties, it's still better than alternatives studied in Chapter 4, Fundamentals of Feature Importance and Impact.

It has three properties that are loosely based on Shapley's:

  • Local accuracy: Equivalent to Shapley's efficiency property.
  • Consistency: Encompasses additivity and substitutability axioms, and, in theory, dummy as well...