Have you ever wondered how to make your machine learning models more trustworthy and reliable? I’m excited to share that a new book, Advanced Conformal Prediction: Reliable Uncertainty Quantification for Real-World Machine Learning, is now available in early access.
Conformal Prediction (CP) is a powerful tool in machine learning that provides rigorous, model-agnostic uncertainty quantification with finite-sample guarantees. But despite its potential, CP remains underused. That’s why I’m thrilled to see this comprehensive guide that covers everything from the fundamentals to advanced methods and deployment.
## What You’ll Learn
This book covers five key areas:
– **Foundations**: An intuitive introduction to CP, calibration, and statistical guarantees.
– **Core methods**: Split/inductive CP for regression and classification, conformalized quantile regression (CQR).
– **Advanced methods**: Weighted CP for covariate shift, EnbPI, blockwise CP for time series, conformal prediction with deep learning (including transformers).
– **Practical deployment**: Benchmarking, scaling CP to large datasets, industry use cases in finance, healthcare, and more.
– **Code & case studies**: Hands-on Jupyter notebooks to bridge theory and application.
## Why Conformal Prediction Matters
When it comes to machine learning, accuracy is just the beginning. We need models that are trustworthy and reliable. Conformal Prediction provides a way to quantify uncertainty, making it an essential tool for anyone working with machine learning.
## From Zero to Advanced Practice
The book’s author noticed a gap in existing resources: papers were too technical, and tutorials too narrow. This book aims to fill that gap, providing a comprehensive guide that takes you from zero knowledge to advanced practice.
If you’re curious about uncertainty quantification or want to learn how to make your models more trustworthy and reliable, I hope you’ll find this book useful.