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

Quantifying uncertainty using conformal prediction

Quantifying the uncertainty of machine learning predictions is becoming increasingly important as machine learning is used more widely in critical applications such as healthcare, finance, and self-driving cars. In these applications, the consequences of incorrect predictions can be severe, making it essential to understand the uncertainty associated with each prediction.

For example, in healthcare, machine learning models are used to make predictions about patient outcomes, such as the likelihood of a disease or the effectiveness of a treatment. These predictions can have a significant impact on patient care and treatment decisions. However, if the model is unable to produce an estimate of its own confidence, it may not be useful and could potentially be risky to rely upon.

In contrast, if the model can provide a measure of its own uncertainty, clinicians can use this information to make more informed decisions about patient...