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

Classifier calibration

Most statistical, machine learning, and deep learning models output predicted class labels, and the models are typically evaluated in terms of their accuracy.

Accuracy is a prevalent measure for assessing the performance of a machine learning classification model. It quantifies the ratio of instances that are correctly identified to the overall count in the dataset. In other words, accuracy tells us how often the model’s predictions align with the true labels of the data.

The accuracy score measures how often the model’s predictions match the true observed labels. It is calculated as the fraction of correct predictions out of all predictions made. Accuracy scores between 0 and 1 quantify how accurate the model’s predictions are compared to the ground truth data. A higher accuracy score close to 1 signifies that the model is performing very accurately overall, with most of its predictions being correct. A lower accuracy approaching 0...