This book is about making machine learning models and their decisions interpretable.
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications.
By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
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.
This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) – Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework ...
Suppose we are given two such methods of generating a salient sub-image for a classification system. How should we measure which method provides a better explanation of what the system is doing? This is an important question, ...
This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, ...
About the book Interpretable AI teaches you to identify the patterns your model has learned and why it produces its results.
Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts of machine learning interpretability along with different types of explainability algorithms.
This is an emerging area that has not fully matured yet and hence the book will draw considerable interest and attention.