Given the use of a low-budget, high-dimensional Bayesian optimization task, it may be reasonable to assume that the points sampled during each optimization campaign tend more towards exploration rather than exploitation. However, for datasets that are highly concentrated (e.g. due to local optimization or exploitation search behavior), the model accuracy may appear unrealistically high. In an extreme case, if the same parameterization is sampled at every iteration, the cross-validated accuracy will only be limited by the stochasticity of the objective function. Thus, use of LOO-CV scaled MAE is better suited for comparing results where exploration is present and the choice of hyperparameters does not cause significant clustering of sampled points. To circumvent this, an alternative cross-validation scheme may be used where domain-informed clusters or groups are used to define cross-validation splits . The direct analogue to LOO-CV is leave-one-group-out crossvalidation (also referred to as leave-one-cluster-out cross-validation), which is used to "[prevent] overly optimistic extrapolative predictive performance" .