where B denotes the number of decision trees in the forest, θ i represents the learned parameters of the i-th tree, and θ = {θ 1 , . . . , θ B } encompasses all tree parameters. For regression tasks, f θi (x) ∈ R denotes the predicted value by the i-th tree, while for classification tasks, f θi (x) ∈ [0, 1] signifies the predicted probability of the positive class. Further details regarding the training procedure of the RF model can be found in Appendix A.