  Safety-critical applications require machine learning models that output
accurate and calibrated probabilities. While uncalibrated deep networks are
known to make over-confident predictions, it is unclear how model confidence is
impacted by the variations in the data, such as label noise or class size. In
this paper, we investigate the role of the dataset quality by studying the
impact of dataset size and the label noise on the model confidence. We
theoretically explain and experimentally demonstrate that, surprisingly, label
noise in the training data leads to under-confident networks, while reduced
dataset size leads to over-confident models. We then study the impact of
dataset heterogeneity, where data quality varies across classes, on model
confidence. We demonstrate that this leads to heterogenous confidence/accuracy
behavior in the test data and is poorly handled by the standard calibration
algorithms. To overcome this, we propose an intuitive heterogenous calibration
technique and show that the proposed approach leads to improved calibration
metrics (both average and worst-case errors) on the CIFAR datasets.
