Decision trees are a simple yet powerful method of machine learning. A binary tree is constructed in which the leaf nodes represent predictions. The internal nodes are decision points. Thus, paths from the root to the leafs represent sequences of decisions that result in an ultimate prediction.

Decision trees can also be used in hierarchical models. For instance, the leafs can instead represent subordinate models. Thus, a path from the root to a leaf node is a sequence of decisions that result in a prediction made by a subordinate model. The subordinate model is only responsible for predicting samples that fall within the leaf.

This post presents an approach for a hierarchical decision tree model with subordinate linear regression models.

Continue reading “Applying Correlation as a Criterion in Hierarchical Decision Trees”