In classification and forecasting with tabular data, one often utilizes tree-based models. This can be competitive with deep neural networks on tabular data [cf. Grinsztajn et al., NeurIPS 2022, arXiv:2207.08815] and, under some conditions, explainable. The explainability depends on the depth of the tree and the accuracy in each leaf of the tree. Here, we train a low-depth tree with the objective of minimising the maximum misclassification error across each leaf node, and then ``suspend'' further tree-based models (e.g., trees of unlimited depth) from each leaf of the low-depth tree. The low-depth tree is easily explainable, while the overall statistical performance of the combined low-depth and suspended tree-based models improves upon decision trees of unlimited depth trained using classical methods (e.g., CART) and is comparable to state-of-the-art methods (e.g., well-tuned XGBoost).

翻译：在处理表格数据的分类与预测任务中，树模型常被采用。此类模型在表格数据上可与深度神经网络相竞争 [参见 Grinsztajn 等人，NeurIPS 2022，arXiv:2207.08815]，并且在某些条件下具备可解释性。其可解释性取决于树的深度及每个叶节点的精度。本文提出方法：训练一棵低深度树，以最小化各叶节点跨类最大误分类率为目标；随后在每个叶节点上“悬挂”额外的树模型（例如不限深度的树）。该低深度树易于解释，而由低深度树与悬挂树模型组成的组合模型，其整体统计性能优于采用经典方法（如CART）训练的不限深度决策树，并与最优方法（如经精调的XGBoost）相当。