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
1
Part 1: Introduction
4
Part 2: Conformal Prediction Framework
8
Part 3: Applications of Conformal Prediction
14
Part 4: Advanced Topics

Various approaches to quantify uncertainty in NLP problems

Multiple methods to quantify uncertainty in NLP problems have been explored to address the challenges of miscalibration and language’s inherent unpredictability.

We will now look at Bayesian approaches to UQ.

Bayesian approaches to uncertainty quantification

Bayesian methods provide a framework for modeling uncertainty. By treating model parameters as distributions rather than fixed values, Bayesian neural networks offer a measure of uncertainty associated with predictions. This probabilistic approach ensures that the model not only gives an estimate but also conveys the confidence or spread of that estimate.

These are some of the examples of Bayesian approaches to UQ.

  • Variational inference is a technique to approximate the posterior distribution of the model parameters, enabling the network to output distributions for predictions.
  • Bayesian neural networks (BNNs) are neural networks with weights...