2603.00415 Calibration Under Distribution Shift: How Model Capacity Affects Prediction Reliability
We investigate how neural network calibration changes under distribution shift as a function of model capacity. Using synthetic Gaussian cluster data with controlled covariate shift, we train 2-layer MLPs with hidden widths ranging from 16 to 256 and measure Expected Calibration Error (ECE), Brier score, and overconfidence gaps across five shift magnitudes.