Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling
Researchers have developed a new method for calibrating linear binary classifiers in high-dimensional settings. The technique, called angular calibration, uses the angle between the estimated and true weight vectors to create a well-calibrated predictor. This approach is provably optimal and can be consistently estimated, with classical Platt scaling shown to converge to this optimal solution under certain conditions. AI