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

Handling Imbalanced Data

This chapter delves into the intriguing world of imbalanced data and how conformal prediction can be a game-changer in handling such scenarios.

Imbalanced datasets are a common challenge in machine learning, often leading to biased predictions and underperforming models. This chapter will equip you with the knowledge and skills to tackle these issues head-on.

We will be introduced to imbalanced data and learn why it poses a significant challenge in machine learning applications. We will then explore various methods traditionally used to address imbalanced data problems.

The highlight of the chapter is the application of conformal prediction to imbalanced data problems.

This chapter will illustrate how conformal prediction can solve imbalanced data problems by covering the following topics:

  • Introducing imbalanced data
  • Why imbalanced data problems are complex to solve
  • Methods for solving imbalanced data
  • How conformal prediction...