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

The validity of probabilistic predictors

We start by summarizing the reasons why unbiased point prediction models are important across various domains and applications:

  • Accuracy and reliability: An unbiased model ensures that the predictions it generates are accurate and reliable on average, meaning that the model is neither systematically overestimating nor underestimating the true values. This accuracy is crucial for making well-informed decisions, minimizing risks, and improving the overall performance of a system.
  • Trust and credibility: Unbiased prediction models help build trust and credibility among stakeholders, as they provide a reliable basis for decision-making. Users can have more confidence in the outputs generated by an unbiased model, knowing that it is not skewed or favoring any specific outcome.
  • Fairness and equity: In some applications, such as finance, healthcare, and human resources, unbiased models are essential to ensure fairness and equity among...