基于超解析线性递归模型的可解释分类
Supersparse Linear Integer Models for Interpretable Classification
摘要：
评分系统是一种分类模型，只需要用户对一些有意义的数字进行加、减、乘即可进行预测。这些模型经常被使用，因为它们是实用和可解释的。在本文中，我们介绍了一种现成的工具来创建评分系统，既准确又可解释，称为超解析线性模型（SLIM）。SLIM是一个离散优化问题，它最小化0-1损失以鼓励高水平的准确性，正则化L0范数以鼓励高水平的稀疏性，并将系数约束为一组可解释的值。我们通过在医学和犯罪学中的应用说明了SLIM评分系统的实用性和可解释性，并表明它们与使用数值实验的最先进的分类模型相比是准确和稀疏的。
英文摘要：
Scoring systems are classification models that only require users to add, subtract and multiply a few meaningful numbers to make a prediction. These models are often used because they are practical and interpretable. In this paper, we introduce an off-the-shelf tool to create scoring systems that both accurate and interpretable, known as a Supersparse Linear Integer Model (SLIM). SLIM is a discrete optimization problem that minimizes the 0-1 loss to encourage a high level of accuracy, regularizes the L0-norm to encourage a high level of sparsity, and constrains coefficients to a set of interpretable values. We illustrate the practical and interpretable nature of SLIM scoring systems through applications in medicine and criminology, and show that they are are accurate and sparse in comparison to state-of-the-art classification models using numerical experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
