  Probabilistic predictions from neural networks which account for predictive
uncertainty during classification is crucial in many real-world and high-impact
decision making settings. However, in practice most datasets are trained on
non-probabilistic neural networks which by default do not capture this inherent
uncertainty. This well-known problem has led to the development of post-hoc
calibration procedures, such as Platt scaling (logistic), isotonic and beta
calibration, which transforms the scores into well calibrated empirical
probabilities. A plausible alternative to the calibration approach is to use
Bayesian neural networks, which directly models a predictive distribution.
Although they have been applied to images and text datasets, they have seen
limited adoption in the tabular and small data regime. In this paper, we
demonstrate that Bayesian neural networks yields competitive performance when
compared to calibrated neural networks and conduct experiments across a wide
array of datasets.
