On sparse optimal regression trees
将最优回归树建模为连续优化问题，在预测精度与局部和全局稀疏性之间寻求平衡，支持成本敏感性和公平性，并通过平滑预测提供连续变量的局部解释。实验表明，该方法在预测精度上优于CART、OLS和LASSO等标准方法，且具有良好的可扩展性。
In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desirable properties for the regression task, such as cost-sensitivity and fairness. Thanks to the smoothness of the predictions, we can derive local explanations on the continuous predictor variables. The computational experience reported shows the outperformance of our approach in terms of prediction accuracy against standard benchmark regression methods such as CART, OLS and LASSO. Moreover, the scalability of our approach with respect to the size of the training sample is illustrated.