optreedt

Optreedt is a computational framework for optimizing decision tree structures, aiming to produce compact, accurate, and interpretable models by jointly optimizing tree structure and splits under a customizable objective. The name reflects its core components: editing allowed at the subtree level, dynamic programming-based optimization, and controlled pruning.

Methodology: The framework defines a composite objective that trades off predictive loss (such as misclassification error or log loss) against a complexity penalty (for example, proportional to the number of nodes or tree depth) and, in some settings, evaluation cost. It seeks a tree that minimizes this objective. Subproblems are solved bottom-up with dynamic programming to determine the best subtree for each candidate node. Editing operations include deleting subtrees, swapping splits, or relocating decision criteria, enabling local refinements that improve the global objective. Pruning is performed under a cost-complexity constraint to prevent overfitting.

Applications: Optreedt is used in domains requiring interpretable models under resource constraints, such as healthcare decision support, fault diagnosis, financial risk scoring, and edge deployment on embedded devices.

Advantages and limitations: The approach can produce compact trees with competitive accuracy and strong interpretability. It is, however, computationally intensive for large datasets or very deep trees, and performance depends on the choice of objective terms and constraints.

See also: decision tree pruning, optimal decision trees, model compression, interpretable machine learning.