Prediction Interval

Prediction intervals aim to quantify the uncertainty associated with machine learning model predictions, providing a range within which the true value is likely to fall with a specified probability. Current research focuses on improving the accuracy and efficiency of these intervals, particularly addressing challenges like bias in models, heteroscedastic noise, and distribution shifts, often employing techniques like conformal prediction, quantile regression, and various neural network architectures. This work is crucial for enhancing the reliability and trustworthiness of machine learning predictions across diverse applications, from healthcare and finance to environmental monitoring and engineering, where understanding uncertainty is paramount for informed decision-making.