All nodes that have a connection with nodes on a lower level, their children nodes, are called branch-nodes (𝒩 + ) or non-leaf nodes and house a mathematical operator. The nodes on the lowest layer in the tree, referred to as leaf-nodes (𝒩 . ), are assigned to a predictor variable 𝑥 0,2 or a constant 𝜖 4 . In the following sections, we will refer to the total number of activated nodes in the tree as "complexity" of the model (except the ones with an identity operator). Each given data point deployed in the SR is described by two parameters: the value of the predictor variables 𝑥 0,2 and the dependent variable value 𝑦 2 , which is predicted by the model for each training data point j. As shown in Figure , the input variables are assigned for selection only at the leaf-nodes, while the dependent variable values are used at the root node (𝑛 = 1) for comparison with the model prediction.