What is the role of recursion in Decision Tree construction?

Decision Trees are built using recursion by repeatedly splitting datasets into smaller subsets until stopping conditions are met.

Tree construction is inherently recursive. Starting from the root node, the algorithm selects the best split and partitions the dataset into subsets. Each subset becomes a child node, and the same process is applied recursively. The recursion terminates when stopping conditions such as max depth, minimum samples, or purity thresholds are met. This recursive divide-and-conquer strategy is fundamental to how trees are structured.