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

Why does deep learning produce miscalibrated predictions?

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is an annual competition where research teams evaluate their algorithms on a given dataset, aiming to push the boundaries of computer vision. 2012 was a watershed moment for the field, marking a significant shift towards the dominance of deep learning in computer vision (https://www.image-net.org/challenges/LSVRC/2012/).

Before the advent of deep learning, computer vision primarily relied on hand-engineered features and traditional machine learning techniques. Algorithms such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and Speeded-Up Robust Features (SURF) were commonly used to extract features from images. These features would then be fed into machine learning classifiers such as Support Vector Machines (SVM) to make predictions. While these methods had their successes, they had significant limitations regarding scalability...