The empirical nature of machine learning models makes it difficult to identify which predictions are most or least likely to be accurate. In the previous section, we established that molecular size correlates with error-a likely effect of the training set being biased towards molecules with nine nonhydrogen atoms. Here, we explore improved metrics for establishing a quantitative estimate of the size of the error for a prediction.