Issue #94 Theory Edwin Dong, Anurag Shinde
Conformal Prediction: Reliable Uncertainty for Machine Learning
Free editionPRO edition
This week’s PRO issue includes a Colab notebook that uses conformal prediction as an uncertainty quantification method, investigating the process from first principles.
As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
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Machine learning systems are now increasingly deployed in high-stakes domains such as healthcare, autonomous vehicles, finance, and security.
In these environments, incorrect predictions can have serious consequences. Yet most machine learning models provide only a single prediction such as a class label or numerical value without conveying how confident the model actually is.
This limitation has motivated the development of uncertainty quantification techniques, which aim to measure how reliable a model’s predictions are.
In this issue, we will look at one of the most powerful and widely applicable uncertainty techniques called conformal prediction, a statistical framework that produces prediction sets with guaranteed coverage.
Unlike the previous methods, which rely on the distribution of the output data, conformal prediction works as a “distribution-free” method.
The paper “A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification” (Angelopoulos and Bates, 2021) provides a practical and accessible introduction to this idea.
It explains how conformal prediction can be applied to basically any machine learning model while providing rigorous statistical guarantees.
This edition is for PRO members.
