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

Evaluating calibration performance

Evaluating the calibration performance of a classifier is crucial to assessing the reliability and accuracy of its probability estimates. Calibration evaluation allows us to determine how well the predicted probabilities align with the true probabilities or likelihoods of the predicted events. Here are some commonly used techniques for evaluating the calibration performance of classifiers:

  • Calibration plot: A calibration plot visually assesses how well a classifier’s predicted probabilities match the true class frequencies. The x axis shows the predicted probabilities for each class, while the y axis shows the empirically observed frequencies for those predictions.

    For a well-calibrated model, the calibration curve should closely match the diagonal, representing a 1:1 relationship between predicted and actual probabilities. Deviations from the diagonal indicate miscalibration, where the predictions are inconsistent with empirical evidence...