As outlined in , the EPIG criterion requires defining a conditional predictive distribution p(y | x) for each x ∈ U r and a target input distribution p ⋆ (x ⋆ ). In the context of RFs, each decision tree f θi is treated as an individual parameter value θ i . Consequently, each prediction f θi (x) can be interpreted as a result of conditioning on θ i . This yields a collection of predictions {f θi (x)} B i=1 conditioned on B parameter values. By averaging over B, we obtain a conditional predictive distribution p(y | x), where y represents the target class label for RF classifiers or the target value for RF regressors.