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

The methods for solving imbalanced data

Addressing the challenge of imbalanced data isn’t just about achieving a balanced class distribution; it’s about understanding the nuances of the problem and adopting a holistic approach that encompasses all facets of model performance. Let us go through the methods for it:

  • Understanding the problem: The first step is a deep understanding of the problem. It’s essential to discern why the data is imbalanced. Is it because of the nature of the data or perhaps due to some external factors or biases in data collection? Recognizing the root cause can offer insights into the most effective strategies.
  • Prioritizing calibration: One critical aspect that’s often overlooked is calibration. A model’s ability to provide probability estimates that reflect true likelihoods is paramount, especially when decisions are based on these probabilities. Ensuring the model is well calibrated is often more crucial than...