DICS模型在保持预测性能的同时，大幅减少了决策树训练时间，对于需要处理大量和高维数据的场景非常有用，值得一试。
DICS是一种基于聚类的框架，用于构建紧凑且信息丰富的候选分割集，显著减少了分类任务的分割搜索空间，同时保持预测性能。实验表明，DICS在合成和基准数据集上实现了可比的准确率，同时大幅减少了训练时间。
DICS: Data-Informed Centroid Splitting for Decision Tree Classifiers
Decision tree-based models are widely used in machine learning due to their interpretability and strong empirical performance. However, training decision trees can be computationally expensive, particularly for large and high-dimensional datasets, largely due to the exhaustive search over candidate splits at each node. To improve computational efficiency, we propose Data-Informed Centroid Splitting (DICS), a clustering-based framework that constructs a compact and informative set of candidate splits using data-driven priors. By incorporating class-aware structure, DICS significantly reduces the split search space for classification tasks while preserving predictive performance. We further provide theoretical analysis showing that under the stated assumptions, DICS does not degrade the performance of classification trees compared to exhaustive split search. DICS can be incorporated into classification trees, random forests, and gradient-boosting models. Extensive experiments demonstrate that DICS achieves comparable accuracy while substantially reducing training time across synthetic and benchmark datasets, highlighting the benefit of integrating data-informed priors into split selection for scalable classification tree learning.