大型多类任务的嵌套二分法校正
On the Calibration of Nested Dichotomies for Large Multiclass Tasks
摘要：
嵌套二分法被用作将多类分类问题转化为一系列二元问题的方法。引入了一个树结构，它递归地将类的集合分成子集，并且二元分类模型学习在每个节点处区分两个类的子集。在本文中，我们证明了这些嵌套的二分法通常表现出较差的概率校准，即使当基础二进制模型校准良好。我们还表明，当二元模型校准不良时，这个问题会加剧。我们讨论了不同的校准策略的有效性，并表明，通过校准内部基础模型和完整的嵌套二分法结构，特别是当类的数量很高时，可以显着提高精度和对数损失。
英文摘要：
Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A tree structure is induced that recursively splits the set of classes into subsets, and a binary classification model learns to discriminate between the two subsets of classes at each node. In this paper, we demonstrate that these nested dichotomies typically exhibit poor probability calibration, even when the base binary models are well calibrated. We also show that this problem is exacerbated when the binary models are poorly calibrated. We discuss the effectiveness of different calibration strategies and show that accuracy and log-loss can be significantly improved by calibrating both the internal base models and the full nested dichotomy structure, especially when the number of classes is high.
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。
