Interpretable Machine Learning with Python

A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models

Key Features

• Extract easy-to-understand insights from any machine learning model
• Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
• Lift the lid on the black box of transformer NLP models to improve your deep learning understanding

Book Description

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, Second Edition is the book for you.

You'll cover the fundamentals of interpretability, its relevance in business, and explore its key aspects and challenges.

See how white-box models work, compare them to black-box and glass-box models, and examine their trade-offs. Get 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, tabular data, time-series, images, or text.

In addition to the step-by-step code, this book will also help you interpret model outcomes using many 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. You'll also look under the hood of the latest NLP transformer models using the Language Interpretability Tool.

By the end of this book, you'll understand ML models better and enhance them through interpretability tuning.

What you will learn

• Recognize the importance of interpretability in business
• Study models that are intrinsically interpretable such as linear models, decision trees, Naive Bayes, and glass-box models, such as EBM and Gami-NET
• Become well-versed in interpreting black-box models with model-agnostic methods
• Use monotonic and interaction constraints to make fairer and safer models
• Understand how to mitigate the influence of bias in datasets
• Discover how to make models more reliable with adversarial robustness
• Understand how transformer models work and how to interpret them

Who This Book Is For

This book is for data scientists, machine learning developers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.

Table of Contents

1. Interpretation, Interpretability and Explainability; and why does it all matter?
2. Key Concepts of Interpretability
3. Interpretation Challenges
4. Global Model-agnostic Interpretation Methods
5. Local Model-agnostic Interpretation Methods
6. Anchor and Counterfactual Explanations
7. Visualizing Convolutional Neural Networks
8. Understanding NLP Transformers
9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
10. Feature Selection and Engineering for Interpretability
11. Bias Mitigation and Causal Inference Methods
12. Feature Selection for Interpretability
13. (N.B. Additional chapters to be confirmed upon publication