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You're reading from  Practical Guide to Applied Conformal Prediction in Python

Product typeBook
Published inDec 2023
PublisherPackt
ISBN-139781805122760
Edition1st Edition
Concepts
Machine Learning
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Author (1)
Valery Manokhin
Valery Manokhin
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Valery Manokhin

Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation 'Machine Learning for Probabilistic Prediction' is read by thousands of people across the world. He is also the creator of "Awesome Conformal Prediction," the most popular resource and GitHub repository for all things Conformal Prediction.
Read more about Valery Manokhin

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Understanding uncertainty quantification

Uncertainty is an inherent part of any prediction, as there are always factors that are unknown or difficult to measure. Predictions are typically made based on incomplete data or models that are unable to capture the full complexity of the real world. As a result, the predictions are subject to various sources of uncertainty, including randomness, bias, and model errors.

To mitigate the risks associated with uncertainty, it is essential to quantify it accurately. By quantifying uncertainty, we can estimate the range of possible outcomes and assess the degree of confidence we can have in our predictions. This information can be used to make informed decisions and to identify areas where further research or data collection is needed.

UQ is a field of study that helps us measure how much we don’t know when we make predictions. UQ tries to estimate the probability of outcomes even if some aspects of the system under study are not known...

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Practical Guide to Applied Conformal Prediction in Python
Published in: Dec 2023Publisher: PacktISBN-13: 9781805122760
© 2023 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Valery Manokhin

Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation 'Machine Learning for Probabilistic Prediction' is read by thousands of people across the world. He is also the creator of "Awesome Conformal Prediction," the most popular resource and GitHub repository for all things Conformal Prediction.
Read more about Valery Manokhin

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