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

Chapter 6: Local Model-Agnostic Interpretation Methods

In the previous two chapters, we dealt exclusively with global interpretation methods. This chapter will foray into local interpretation methods, which are there to explain why a single prediction or a group of predictions was made. It will cover how to leverage SHapley Additive exPlanations' (SHAP's) KernelExplainer and also, another method called Local Interpretable Model-agnostic Explanations (LIME) for local interpretations. We will also explore how to use these methods with both tabular and text data.

These are the main topics we are going to cover in this chapter:

  • Leveraging SHAP's KernelExplainer for local interpretations with SHAP values
  • Employing LIME
  • Using LIME for natural language processing (NLP)
  • Trying SHAP for NLP
  • Comparing SHAP with LIME