  Most machine learning models operate under the assumption that the training,
testing and deployment data is independent and identically distributed
(i.i.d.). This assumption doesn't generally hold true in a natural setting.
Usually, the deployment data is subject to various types of distributional
shifts. The magnitude of a model's performance is proportional to this shift in
the distribution of the dataset. Thus it becomes necessary to evaluate a
model's uncertainty and robustness to distributional shifts to get a realistic
estimate of its expected performance on real-world data. Present methods to
evaluate uncertainty and model's robustness are lacking and often fail to paint
the full picture. Moreover, most analysis so far has primarily focused on
classification tasks. In this paper, we propose more insightful metrics for
general regression tasks using the Shifts Weather Prediction Dataset. We also
present an evaluation of the baseline methods using these metrics.
