Book Image

Practical Guide to Applied Conformal Prediction in Python

By : Valery Manokhin
4 (1)
Book Image

Practical Guide to Applied Conformal Prediction in Python

4 (1)
By: Valery Manokhin

Overview of this book

In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.
Table of Contents (19 chapters)
Free Chapter
Part 1: Introduction
Part 2: Conformal Prediction Framework
Part 3: Applications of Conformal Prediction
Part 4: Advanced Topics


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


2012 ImageNet competition 152


absolute error 35

Adaptive Prediction Sets (APS) 162-164

calibration phase 163

prediction phase 164

quantile threshold, determining 163

adaptive synthetic sampling (ADASYN) 184

aleatoric uncertainty 16, 92, 150

examples 16

AlexNet revolutionary 152

algorithms, for multi-class classification

decision trees 194

Naive Bayes 194

neural networks (NNs) 194

Area Under the Receiver Operating Characteristic Curve (AUC-ROC) 196

AvgC 32


Bayesian approaches 129

Bayesian methods 96, 98

Bayesian Neural Networks (BNNs) 156, 173

Bayesian PI construction techniques

Bayesian forecasting models 129

Monte Carlo Markov Chain (MCMC) sampling 129

Bidirectional Encoder Representations from Transformers (BERT...