A new, much needed book on Applied Conformal Prediction in Python
Valery Manokhin’s great interest and expertise in conformal prediction is undeniable to all who follow him on LinkedIn and Medium. So it was no surprise for me to see the masterful work that he made in his book “Practical Guide to Applied Conformal Prediction in Python”.
The book starts with two solid introductory chapters where the author explains the basics and motivation for his subject matter in a brilliant, clear way. These chapters are followed by 3 insightful and brilliant chapters on the conformal prediction framework. I particularly liked the chapter on types of conformal predictors.
Applications of conformal prediction is the next section of the book covering conformal prediction for classification, regression, time series and forecasting, computer vision, and natural language processing. This wide range of applications gives the reader a deep perspective on the applications of conformal prediction. Valery writes these chapters very well and provides many very useful insights.
Advanced topics is the last section of the book where handling imbalanced data and multi-class conformal prediction are covered. These two chapters are among the best in the book, where Valery’s great expertise is visible.
This book is certainly a great contribution to the area, along with Christoph Molnar’s book on the subject. Practical applications of conformal prediction receive their first masterful, outstanding, and clear exposition. I highly recommend this book. It was a true delight to read it.
You can get a copy of this book at the Amazon link below:
Christoph Molnar’s brilliant book on the subject, “Introduction To Conformal Prediction With Python: A Short Guide For Quantifying Uncertainty Of Machine Learning Models”, is a great companion to this book. You can get a copy at Amazon at the link below:
