Which aspect of a decision tree does multi-way splits affect?

Multi-way splits in a decision tree refer to the ability to divide a node into more than two branches based on the values of a predictor variable. This can significantly influence the structure of the tree.

When multi-way splits are allowed, the tree can create more branches from a single node, potentially leading to a greater number of terminal nodes or leaves being produced. This often results in a tree that can be taller, meaning that it has more levels from the root to the leaves. Each level represents a decision point, and as the number of splits increases, the complexity of the tree can increase, leading to a higher tree height.

It's important to distinguish tree height from tree depth; while both terms relate to the structure of the tree, tree height typically refers to the total number of levels from the root node to the deepest leaf node, whereas depth often refers to the number of edges from the root node to the leaf. However, in the context of decision trees, when multi-way splits are implemented, they primarily affect the overall height by introducing more levels of branching.

Node strength, while related to how well a node predicts the target variable, does not directly correlate with the branching structure of the tree. Variable importance reflects how influential a particular predictor is for the