Prediction intervals with gradient boosting machine
Introduction
Machine learning methods, such as ensemble decision trees, are widely used to predict outcomes based on data. However, these methods often focus on providing point predictions, which limits their ability to quantify prediction uncertainty. In many applications, such as healthcare and finance, the goal is not only to predict accurately but also to assess the reliability of those predictions. Prediction intervals, which provide lower and upper bounds such that the true response lies within them with high probability, are a reliable tool for quantifying prediction accuracy. An ideal prediction interval should meet several criteria: it should offer valid coverage (defined below) without relying on strong distributional assumptions, be informative by being as narrow as possible for each observation, and be adaptive—provide wider intervals for observations that are “difficult” to predict and narrower intervals for “easy” ones. Read more…