“…RFR is one of the most widely used ensemble supervised ML algorithms that combine multiple decision trees (Figure 8) for performing regression tasks with continuous target variables (Biau, 2012; Breiman,
2001; Pwavodi et al.,
2023). Breiman (
2001) proposed this ensemble method which independently builds each decision tree and trained on a random subset
where X = ( x 1 , x 2 , …, x n ), Θ denotes a random subset of input features, D n is the training data set and E Θ denotes expectation with respect to the random parameter; it is introduced by selecting different subsets of features (represented by Θ) and different data subsets (represented by r n ( X , Θ, D n )), r n represents the prediction made by an individual decision tree within a random forest ensemble (Figure 8).…”