Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}. Quantifying uncertainty is especially critical in real-world settings, which often involve distributions that are skewed from the training distribution due to a variety of factors including sample bias and non-stationarity. In such settings, well calibrated uncertainty estimates convey information about when a model's output should (or should not) be trusted. Many probabilistic deep learning methods, including Bayesian-and non-Bayesian methods, have been proposed in the literature for quantifying predictive uncertainty, but to our knowledge there has not previously been a rigorous large-scale empirical comparison of these methods under conditions of distributional skew. We present a large-scale benchmark of existing state-of-the-art methods on classification problems and investigate the effect of distributional skew on accuracy and calibration. We find that traditional post-hoc calibration falls short and some Bayesian methods are intractable for very large data. However, methods that marginalize over models give surprisingly strong results across a broad spectrum.

• Machine Intelligence