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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.

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

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

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View plans & pricing

Frequently bought together

Practical Guide to Applied Conformal Prediction in Python

€27.99 ~~€37.99~~

Interpretable Machine Learning with Python

€37.99

Causal Inference and Discovery in Python

€20.98 ~~€29.99~~

Total € 86.96 105.97 19.01 saved

Jeremy Aug 04, 2024

Outstanding book to learn about Conformal Prediction! This is the book I have been looking for to help me understand prediction intervals, uncertainty quantification and producing more trustworthy models.

Subscriber review

Krishna A Apr 08, 2024

The author does a good job of covering the general framework and main ideas of conformal prediction(CP) in Chapters 1-5 followed by a wide application section on classification, regression, time series, Vision, NLP from Chapters 6-10. I’d recommend jumping to the chapter corresponding to your task at hand, as the general framework is task agnostic and each chapter is self contained.Here’s a high level summary of what conformal prediction offers, and how to apply it in your own pipelines:What it offers:- Conformal prediction is a general framework for quantifying uncertainty of your ML model, it allows you to go from point predictions to prediction sets.-These prediction sets come with guarantees of coverage i.e, the true label lies within this prediction set with probability at least 1-alpha. This alpha is set by you, for e.g. if you want a 95% guarantee of coverage you set alpha to 0.05.-These prediction sets are adaptive i.e. The size of the set is an indicator of how uncertain your model is about a particular instanceHow to use it:-Train your model as usual on the training data.-Calibrate your model using a held out portion of the dataset.-To do so, pick a nonconformity measure for your task, this roughly measures how different a new datapoint is from the existing data points.

Amazon Verified review

Al Jan 02, 2024

Teaches cutting edge technology concepts in an easy manner and detailed oriented examples using python, must read for this advanced topic in ML

Amazon Verified review

S M Feb 13, 2024

Having delved deep into "Practical Guide to Applied Conformal Prediction in Python" by Valery Manokhin, I'm compelled to share my enthusiasm for this standout resource in the machine learning community. This book is a comprehensive exploration of Conformal Prediction (CP), a topic of paramount importance for anyone keen on elevating their data science and machine learning projects to the next level.The breadth and depth of topics covered in this book are truly impressive. Manokhin starts with the fundamental concepts of CP, ensuring readers understand the theoretical underpinnings before diving into its practical applications. What sets this book apart is its holistic approach, encompassing a wide array of applications from binary classification and regression to the more complex realms of time series forecasting, computer vision, and natural language processing (NLP).The chapters on time series forecasting and computer vision are particularly enlightening, showcasing CP's versatility and power in handling diverse and challenging datasets. The book goes beyond the basics, delving into nuanced topics like imbalanced data and multi-class CP, areas often overlooked in other texts. This depth ensures that readers are not only equipped with theoretical knowledge but also with the practical skills to apply CP in real-world scenarios.Practical examples peppered throughout the book, all in Python, reinforce the material, allowing readers to see CP in action. These examples are not just academic exercises; they are drawn from real-world datasets, making the lessons learned directly applicable to one's own industry projects.Moreover, the focus on enhancing prediction reliability through CP is timely and critical. In an era where data-driven decision-making is paramount, the ability to accurately quantify and communicate prediction uncertainty is a game-changer. This book empowers readers to do just that, boosting their confidence in the models they build and deploy.In conclusion, "Practical Guide to Applied Conformal Prediction in Python" is an invaluable asset for data scientists, ML engineers, academics, and anyone interested in advancing their understanding of uncertainty quantification in machine learning. Whether you're a novice seeking to learn about CP or a seasoned practitioner aiming to refine your skills, this book is a must-have. It's not just a guide; it's a comprehensive toolkit that will undoubtedly enhance your machine learning endeavors.

Amazon Verified review

Dr. Armando Fandango Jan 09, 2024

"A Practical Guide to Applied Conformal Prediction" is a comprehensive exploration of conformal prediction techniques that caters to both novice and experienced data scientists. The author's commitment to demystifying complex concepts is evident throughout the book, making it a valuable resource in the realm of predictive modeling.The book's strength lies in its systematic approach, covering a wide range of topics from the fundamentals to advanced applications. The inclusion of practical examples and case studies provides a hands-on learning experience, facilitating a deeper understanding of conformal prediction in real-world scenarios.The clarity in presentation and the use of illustrative examples make this book accessible, even for readers without extensive prior knowledge in the field. However, a more pronounced emphasis on the mathematical underpinnings may have benefitted readers aiming for a deeper theoretical understanding.One notable aspect is the inclusion of exercises and challenges at the end of each chapter, encouraging readers to apply what they've learned. This interactive element adds a practical dimension to the learning process, reinforcing the theoretical concepts covered.While the book offers a commendable overview of conformal prediction, a more nuanced discussion of potential challenges and limitations, as well as alternative approaches, could have enhanced its completeness.In summary, "A Practical Guide to Applied Conformal Prediction" is a commendable resource for those looking to delve into the intricacies of conformal prediction. Its thorough coverage, practical examples, and interactive exercises make it a valuable tool in the data scientist's toolkit. With a well-deserved 5/5 rating, this book stands out for its accessibility and practical applicability in the field of predictive modeling.

Amazon Verified review

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

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