Prediction tasks in personalized medicine require models that combine accuracy and interpretability. We propose an integer optimization approach for building sparse regression models with enforced coordination, using data partitioned among leaves in a prediction tree. We show that the method recovers the true underlying relationship between observations and target variables in large-scale synthetic data in seconds. We apply our method to several real-world medical prediction problems and observe that the additional structure imposed provides a substantial gain in interpretability, at a low cost to accuracy.

中文翻译：

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集群上的稀疏回归：SparClur

个性化医疗中的预测任务需要结合准确性和可解释性的模型。我们提出了一种整数优化方法，用于构建具有强制协调的稀疏回归模型，使用在预测树中的叶子之间分区的数据。我们表明，该方法可以在几秒钟内恢复大规模合成数据中观测值与目标变量之间的真实潜在关系。我们将我们的方法应用于几个现实世界的医学预测问题，并观察到所施加的附加结构在可解释性方面提供了实质性的增益，而准确性成本较低。