Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

by Serg Masís, Aleksander Molak (Foreword by), Denis Rothman (Foreword by)

Overview

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.

Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.

In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.

By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

Product Details

ISBN-13:	9781803243627
Publisher:	Packt Publishing
Publication date:	10/31/2023
Sold by:	Barnes & Noble
Format:	eBook
Pages:	606
File size:	46 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.
Causality Advocate, Bestselling Author, AI Researcher & Strategist
Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author

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. Global Model-agnostic Interpretation Methods
5. Local Model-agnostic Interpretation Methods
6. Anchors and Counterfactual Explanations
7. Visualizing Convolutional Neural Networks
8. Interpreting 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. Monotonic Constraints and Model Tuning for Interpretability
13. Adversarial Robustness
14. What's Next for Machine Learning Interpretability?