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

Various approaches to producing PIs

PIs are an essential tool in forecasting, allowing practitioners to understand the range within which future observations are likely to fall. Various approaches have been developed to produce these intervals, each with advantages, applications, and challenges. This section will explore the most prominent techniques for creating PIs.

Parametric approaches

Parametric approaches make specific assumptions about the distribution of forecast errors to derive PIs. Some standard techniques in this category are as follows:

  • Normal distribution assumptions: By assuming that the forecast errors follow a normal distribution, we can compute symmetric PIs based on standard errors and critical values from the normal distribution.
  • Time series models: Models such as ARIMA and exponential smoothing can generate PIs by modeling the underlying stochastic process and using the estimated parameters to produce intervals.
  • Generalized linear models ...