How do Decision Trees perform feature interactions modeling?

Decision Trees naturally capture feature interactions through hierarchical splits without explicitly defining interaction terms.

Unlike linear models that require manual interaction features, decision trees implicitly learn interactions by splitting sequentially on different features. For example, a split on income followed by a split on age creates an interaction between income and age. This hierarchical partitioning allows trees to model nonlinear and conditional dependencies effectively. However, deep trees may overfit these interactions if not regularized.