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

Various approaches to quantify uncertainty in computer vision problems

Uncertainty quantification in computer vision is crucial for ensuring vision-based systems’ reliability and safety, especially when deployed in critical applications. Over the years, various approaches have been developed to address and quantify this uncertainty. Here’s a look at some of the most prominent methods:

  • Bayesian Neural Networks (BNNs): These neural networks treat weights as probability distributions rather than fixed values. By doing so, they can provide a measure of uncertainty for their predictions. During inference, multiple forward passes are made with different weight samples, producing a distribution of outputs that capture the model’s uncertainty.
  • Monte Carlo dropout: Monte Carlo dropout involves performing dropout during inference. By running the network multiple times with dropout and averaging the results, a distribution over the outputs is obtained, which can...