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Practical Guide to Applied Conformal Prediction in Python
Practical Guide to Applied Conformal Prediction in Python

Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications

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Practical Guide to Applied Conformal Prediction in Python

Part 1: Introduction

This part will introduce you to conformal prediction. It will explain in detail the type of problem that conformal prediction can address and outline the general ideas on which it is based.

This part has the following chapters:

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Key benefits

  • Master Conformal Prediction, a fast-growing ML framework, with Python applications
  • Explore cutting-edge methods to measure and manage uncertainty in industry applications
  • Understand how Conformal Prediction differs from traditional machine learning

Description

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.

Who is this book for?

Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.

What you will learn

  • The fundamental concepts and principles of conformal prediction
  • Learn how conformal prediction differs from traditional ML methods
  • Apply real-world examples to your own industry applications
  • Explore advanced topics - imbalanced data and multi-class CP
  • Dive into the details of the conformal prediction framework
  • Boost your career as a data scientist, ML engineer, or researcher
  • Learn to apply conformal prediction to forecasting and NLP
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 20, 2023
Length: 240 pages
Edition : 1st
Language : English
ISBN-13 : 9781805122760
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Product Details

Publication date : Dec 20, 2023
Length: 240 pages
Edition : 1st
Language : English
ISBN-13 : 9781805122760
Category :
Languages :
Tools :

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Table of Contents

18 Chapters
Part 1: Introduction Chevron down icon Chevron up icon
Chapter 1: Introducing Conformal Prediction Chevron down icon Chevron up icon
Chapter 2: Overview of Conformal Prediction Chevron down icon Chevron up icon
Part 2: Conformal Prediction Framework Chevron down icon Chevron up icon
Chapter 3: Fundamentals of Conformal Prediction Chevron down icon Chevron up icon
Chapter 4: Validity and Efficiency of Conformal Prediction Chevron down icon Chevron up icon
Chapter 5: Types of Conformal Predictors Chevron down icon Chevron up icon
Part 3: Applications of Conformal Prediction Chevron down icon Chevron up icon
Chapter 6: Conformal Prediction for Classification Chevron down icon Chevron up icon
Chapter 7: Conformal Prediction for Regression Chevron down icon Chevron up icon
Chapter 8: Conformal Prediction for Time Series and Forecasting Chevron down icon Chevron up icon
Chapter 9: Conformal Prediction for Computer Vision Chevron down icon Chevron up icon
Chapter 10: Conformal Prediction for Natural Language Processing Chevron down icon Chevron up icon
Part 4: Advanced Topics Chevron down icon Chevron up icon
Chapter 11: Handling Imbalanced Data Chevron down icon Chevron up icon
Chapter 12: Multi-Class Conformal Prediction Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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N/A Jan 23, 2024
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Very informative book. Further steps could be taken to elaborate on a number of concepts. The CV and NLP sections have little material and could use a lot of work in later editions with later research in the application of CP in these fields.
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