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

Basic components of a conformal predictor

We will now look at the basic components of a conformal predictor:

  • Nonconformity measure: The nonconformity measure is a function that evaluates how much a new data point differs from the existing data points. It compares the new observation to either the entire dataset (in the full transductive version of conformal prediction) or the calibration set (in the most popular variant – ICP. The selection of the nonconformity measure is based on a particular machine learning task, such as classification, regression, or time series forecasting, as well as the underlying model. This chapter will examine several nonconformity measures suitable for classification and regression tasks.
  • Calibration set: The calibration set is a portion of the dataset used to calculate nonconformity scores for the known data points. These scores are a reference for establishing prediction intervals or regions for new test data points. The calibration...