In classification and forecasting with tabular data, one often utilizes tree-based models. Those 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. Decision trees containing leaves with unbalanced accuracy can provide misleading explanations. Low-accuracy leaves give less valid explanations, which could be interpreted as unfairness among explanations. Here, we train a shallow tree with the objective of minimizing the maximum misclassification error across each leaf node. Then, we extend each leaf with a separate tree-based model. The shallow tree provides a global explanation, while the overall statistical performance of the shallow tree with extended leaves 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：在表格数据的分类与预测任务中，常采用基于树的模型。此类模型在表格数据上可与深度神经网络相媲美[参见Grinsztajn等, NeurIPS 2022, arXiv:2207.08815]，并在特定条件下具备可解释性。可解释性取决于树的深度及每个叶节点的准确率。包含叶节点准确率不均衡的决策树可能提供具有误导性的解释。低准确率叶节点会产生效度不足的解释，这可能被解读为解释过程中的不公平现象。本文通过最小化每个叶节点上的最大误分类误差为目标训练浅层树，随后为每个叶节点扩展一个独立的基于树的模型。浅层树提供全局解释，而扩展叶节点的浅层树整体统计性能优于采用经典方法（如CART）训练的无限深度决策树，且与先进方法（如精心调参的XGBoost）相当。