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

To get the most out of this book

You will need a working Python environment on your computer. We recommend using Python 3.6 or later.

Ensure that you have essential libraries, such as scikit-learn, NumPy, and Matplotlib, installed. If not, you can easily install them using Conda or pip.

The notebooks can be run both locally or by using Google Colab (https://colab.research.google.com).

Software/hardware covered in the book

Operating system requirements

Python

Windows, macOS, or Linux

Colab (to run notebooks in Google Cloud)

Windows, macOS, or Linux

MAPIE

Windows, macOS, or Linux

Amazon Fortuna

Windows, macOS, or Linux

NIxtla statsforecast

Windows, macOS, or Linux

NeuralProphet

Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.