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

Solving imbalanced data problems by applying conformal prediction

Conformal prediction is a technique that can be applied to handle imbalanced data problems. Here are a few ways it can be used:

  • Graceful handling of imbalanced datasets: conformal prediction can gracefully handle large imbalanced datasets. It strictly defines the level of similarity needed, removing any ambiguity. It can handle severely imbalanced datasets with ratios of 1:100 to 1:1000 without oversampling or undersampling.
  • Local clustering conformal prediction (LCCP): LCCP incorporates a dual-layer partitioning approach within the conformal prediction framework. Initially, it segments the imbalanced training dataset into subsets based on class taxonomy. Then, it further divides the examples from the majority class into subsets using clustering techniques. The goal of LCCP is to offer reliable confidence levels for its predictions while also enhancing the efficiency of the prediction process.
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