Next-Depth Lookahead Tree

This paper proposes the Next-Depth Lookahead Tree (NDLT), a single-tree model designed to improve performance by evaluating node splits not only at the node being optimized but also by evaluating the quality of the next depth level. Conventional decision trees (DTs) rely on greedy node-by-node partitioning, which often fails to ensure global optimality of the tree and is prone to local optima when early splits are suboptimal. To overcome this limitation, NDLT employs a next-depth lookahead strategy that jointly considers the immediate impurity reduction at the parent node and the expected impurity reduction at its child nodes. Empirical evaluation on diverse and complex datasets, including high-dimensional and imbalanced cases, demonstrates that NDLT achieves performance comparable to or better than classical DTs and ensemble models such as Random Forests, XGBoost, and Light-GBM. These results show that NDLT preserves the interpretability of a single tree while delivering robust predictive accuracy, making it an effective approach for real-world applications where both transparency and performance are required. These authors contributed equally to this work. Introduction Decision Tree (DT) represents one of the earliest and most established algorithms in the field of machine learning, valued for its simplicity, inter-pretability, and broad applicability [1].