Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data
This work addresses the need for better evaluation methods for model robustness in regression tasks, which is incremental as it extends existing analysis from classification to regression.
The paper tackles the problem of evaluating machine learning models' predictive uncertainty and robustness to distributional shift in real-world data, proposing new metrics for regression tasks and demonstrating their application on the Shifts Weather Prediction Dataset.
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.