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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
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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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Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

2012 ImageNet competition 152

A

absolute error 35

Adaptive Prediction Sets (APS) 162-164

calibration phase 163

prediction phase 164

quantile threshold, determining 163

adaptive synthetic sampling (ADASYN) 184

aleatoric uncertainty 16, 92, 150

examples 16

AlexNet revolutionary 152

algorithms, for multi-class classification

decision trees 194

Naive Bayes 194

neural networks (NNs) 194

Area Under the Receiver Operating Characteristic Curve (AUC-ROC) 196

AvgC 32

B

Bayesian approaches 129

Bayesian methods 96, 98

Bayesian Neural Networks (BNNs) 156, 173

Bayesian PI construction techniques

Bayesian forecasting models 129

Monte Carlo Markov Chain (MCMC) sampling 129

Bidirectional Encoder Representations from Transformers (BERT...

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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