Fig. 2

Shows a TreeNet modeling process which starts with a very small tree containing 2 terminal nodes and this model normally contains 3–5 splits in a tree, generating 4–6 terminal nodes. It computes prediction errors for the model for every record in the dataset and grows second small tree to predict the residuals from the first tree. This process repeats to grow a sequence of trees till it generates an optimal tree for the dataset