Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples

by Serg Masís

Overview

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.

Product Details

ISBN-13:	9781800206571
Publisher:	Packt Publishing
Publication date:	03/26/2021
Sold by:	Barnes & Noble
Format:	eBook
Pages:	736
File size:	18 MB
Note:	This product may take a few minutes to download.

About the Author

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.

Table of Contents

Table of Contents

1. Interpretation, Interpretability and Explainability; and why does it all matter?
2. Key Concepts of Interpretability
3. Interpretation Challenges
4. Fundamentals of Feature Importance and Impact
5. Global Model-Agnostic Interpretation Methods
6. Local Model-Agnostic Interpretation Methods
7. Anchor and Counterfactual Explanations
8. Visualizing Convolutional Neural Networks
9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
10. Feature Selection and Engineering for Interpretability
11. Bias Mitigation and Causal Inference Methods
12. Monotonic Constraints and Model Tuning for Interpretability
13. Adversarial Robustness
14. What's Next for Machine Learning Interpretability?