Practical Guide to Applied Conformal Prediction in Python
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Free ChapterPart 1: Introduction
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Chapter 1: Introducing Conformal Prediction
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Chapter 2: Overview of Conformal Prediction
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Part 2: Conformal Prediction Framework
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Chapter 3: Fundamentals of Conformal Prediction
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Chapter 4: Validity and Efficiency of Conformal Prediction
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Chapter 5: Types of Conformal Predictors
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Part 3: Applications of Conformal Prediction
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Chapter 6: Conformal Prediction for Classification
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Chapter 7: Conformal Prediction for Regression
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Chapter 8: Conformal Prediction for Time Series and Forecasting
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Chapter 9: Conformal Prediction for Computer Vision
- Chapter 9: Conformal Prediction for Computer Vision
- Uncertainty quantification for computer vision
- Why does deep learning produce miscalibrated predictions?
- Various approaches to quantify uncertainty in computer vision problems
- The superiority of conformal prediction in uncertainty quantification
- Conformal prediction for computer vision
- Building computer vision classifiers using conformal prediction
- Summary
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Chapter 10: Conformal Prediction for Natural Language Processing
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Part 4: Advanced Topics
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Chapter 11: Handling Imbalanced Data
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Chapter 12: Multi-Class Conformal Prediction
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Index
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About 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.
- Publication date:
- December 2023
- Publisher
- Packt
- Pages
- 240
- ISBN
- 9781805122760
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