What is ensemble calibration vs accuracy tradeoff?

Calibration ensures probability correctness, while accuracy measures correct classifications; improving one can sometimes hurt the other.

Highly complex ensembles like boosting often achieve high accuracy but poorly calibrated probabilities. Calibration techniques adjust probability outputs without changing decision boundaries. However, improving calibration (e.g., smoothing probabilities) may slightly reduce accuracy. The tradeoff is crucial in risk-sensitive domains like healthcare or finance where probability reliability matters more than raw accuracy.