Constructing Valid and Informative Prediction Intervals for Regression Problems

Prediction intervals are crucial for quantifying uncertainty in regression problems, but ensuring their validity and calibration is challenging. This study reviews and compares four main classes of methods - Bayesian, ensemble, direct interval estimation, and conformal prediction - to construct well-calibrated prediction intervals without being overly conservative.