Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples 2nd ed. Edition
by: Serg Masís (Author), Aleksander Molak (Foreword), Denis Rothman (Foreword)
Publisher finelybook 出版社: Packt Publishing; 2nd ed. edition (October 31, 2023)
Language 语言: English
Print Length 页数: 606 pages
ISBN-10: 180323542X
ISBN-13: 9781803235424
Book Description
A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.
Purchase of the print or Kindle book includes a free eBook in PDF format.
Key Features
Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores
Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods
Analyze and extract insights from complex models from CNNs to BERT to time series models
Book Description
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.
What you will learn
Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty
Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers
Use monotonic and interaction constraints to make fairer and safer models
Understand how to mitigate the influence of bias in datasets
Leverage sensitivity analysis factor prioritization and factor fixing for any model
Discover how to make models more reliable with adversarial robustness
Who this book is for
This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence 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, Kev Concepts of Interpret ability
3. Interpret ation Challenges
4. Global vlodel-ag nosti c Interpret ation Ivlethods
5. Local vlodel-ag nostic Int erpret ati on [vlet hods
6. Anchors and Counterfactual Explanations
7. Visualizing Convolutional Neural Networks
8. Interpreting NLp Transformers
9. Interpret ation [vlethods for vlultivariate Forecasting and Sensitivity Analysis
10. Feature Selection and Engineering for Interpretability
11. Bias Mitigation and Causal Inference Methods
12. Monot onic Constraints and Model Tuning for Interpret ability
13. Acversarial Robustness
14. what’s Next for Mlachine Learning Interpret ability?
Review
“This book dives deep into these fundamental concepts that need to be demystified for beginners and advanced specialists. Serg Masís takes the time to help the reader understand the difference between interpretability and explainability. By the end of the book, you will be able to face the challenges of real-life AI implementations that require interpretability for legal reasons and to gain user trust.”
—
Denis Rothman, AI Ethicist and Bestselling Author
“Serg is one of those authors who brings true passion to their work. To the best of my knowledge, his new book, Interpretable Machine Learning with Python, Second Edition offers the most systematic, clear, and comprehensive coverage of explainability and interpretability methods in Python available on the market. Even if you’re a seasoned practitioner, you’ll likely learn something new from this book.”
—
Aleksander Molak, Author of Causal Inference and Discovery in Python, Creator of CausalPython.io
About the Author
Amazon page
