Isotonic Regression

Isotonic regression is a statistical method used to fit a monotonically increasing (or decreasing) function to data, primarily aiming to calibrate model predictions to improve their reliability and interpretability. Current research focuses on efficient algorithms like the Pool Adjacent Violators Algorithm (PAVA) and its variants, exploring applications in diverse fields such as object detection calibration, classifier calibration, and uncertainty quantification in regression. These advancements enhance the accuracy and trustworthiness of machine learning models across various applications, particularly where well-calibrated probabilities are crucial for decision-making.