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

Uncertainty quantification for regression problems

After completing this chapter, whenever you predict any continuous variable, you’ll be equipped to add a layer of robustness and reliability to your predictions. Understanding and quantifying this uncertainty is crucial for several reasons:

  • Model interpretability and trust: Uncertainty quantification helps us understand the reliability of our model predictions. By providing a range of possible outcomes, we can build trust in our model’s predictions and interpret them more effectively.
  • Decision-making: In many practical applications of regression analysis, decision-makers must rely on something other than point estimates. They often need to know the range within which the actual value will likely fall with a certain probability. This range, or prediction interval, provides crucial information about the uncertainty of the prediction and aids in risk management.
  • Model improvement: Uncertainty can highlight...