Interpretable Machine Learning is a comprehensive guide to understanding and interpreting machine learning models written by Christoph Molnar. The book covers a wide range of topics related to interpretable machine learning, including model interpretability, model-agnostic methods, and post-hoc interpretation techniques.
The book begins by introducing readers to the importance of model interpretability in machine learning, explaining how interpretability can help to build trust in machine learning models and improve their adoption in real-world applications. The author then moves on to cover more advanced topics, such as different types of model interpretability, including global and local interpretability, and the trade-offs involved in different interpretability methods.
One of the unique features of this book is its focus on practical examples. The author provides numerous examples and code snippets throughout the book, demonstrating how to use different methods and techniques to interpret machine learning models in practice. The examples are clear and concise, making it easy for readers to understand and follow along.
Another great aspect of this book is its accessibility. The author uses simple, easy-to-understand language and explains complex concepts in a way that is easy to follow. This makes the book ideal for beginners who are just starting to learn about interpretable machine learning, as well as more experienced machine learning practitioners who want to deepen their knowledge.
The book also covers important topics such as model-agnostic methods, including feature importance and permutation importance, and post-hoc interpretation techniques, such as LIME and SHAP. The author provides advice on how to choose the right interpretability method for a given problem and how to evaluate the interpretability of machine learning models.
Overall, Interpretable Machine Learning is an excellent resource for anyone interested in understanding and interpreting machine learning models. The book is well-written, easy to read, and provides a wealth of information and practical advice for anyone looking to improve their understanding of machine learning interpretability. Whether you’re a data scientist, a machine learning practitioner, or a researcher, this book is a must-read for anyone interested in interpretable machine learning.