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

Understanding inductive conformal predictors

ICP is a variant of conformal prediction that provides valid predictive regions under the same assumptions as classical conformal prediction and has the added benefit of improved computational efficiency, which is particularly useful when dealing with large datasets.

ICPs present a highly efficient and effective solution within the realm of machine learning. They provide a form of conformal prediction that caters to larger datasets, making it highly suitable for real-world applications that involve extensive data volumes. ICPs divide the dataset into training and calibration sets during the model-building process. The training set is used to develop the model, while the calibration set helps calculate the nonconformity scores. This two-step process optimizes computation and delivers precise prediction regions.

Figure 5.3 – Inductive conformal prediction

Figure 5.3 – Inductive conformal prediction

A predictive model, such as a neural network...