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

Choosing the right conformal predictor

Both classical and inductive conformal predictors offer valuable approaches to building reliable machine learning models. However, they each come with unique strengths and weaknesses.

Classical transductive conformal predictors are highly adaptable and do not make any assumptions about data distribution. However, they tend to be computationally expensive, requiring the model’s retraining for each new prediction.

Inductive conformal predictors, conversely, are computationally more efficient, as they only require the model to be trained once.

Choosing the right conformal predictor largely depends on the specific requirements of the problem at hand. Some considerations might include the following:

  • Computation resources: If computation resources or time are a concern, inductive conformal predictors might be more suitable due to their reduced computational cost
  • Data size: For smaller datasets, classical conformal predictors...