FIGURE 1.

Instance‐wise ensembles. (a) Here, we represent the density of the training features for three separate models: M1, M2, and M3. Given new test points A, B, and C, we need to construct predictions from these models. A is well‐represented by both M2 and M3, whereas B only has significant density under M3. C looks like none of the models will be able to make confident predictions. (b) Different models are useful for new patients. Population pharmacokinetic models are often trained on certain demographic groups given the studies that are designed for data collection. For a new patient who does not necessarily fit into one of the existing demographics, different models may be more or less relevant and accurate. Naive ensembles ignore this fact and always incorporate evenly the predictions of each model, SMC on the other hand aims to up‐weight the models that would appear to be more relevant. SMC, Synthetic Model Combination.