使用提升树评估模型校准
Assessing model calibration with boosting trees
AI总结：
提出利用提升树检验回归模型校准与自校准的必要条件，在保险数据集上验证了方法的有效性。
摘要：
回归建模的主要目标在于近似给定一组特征下响应的条件均值。如果得到的均值估计对于几乎所有特征集都匹配真实条件均值，则称回归函数是校准的。在实践中，由于通常处理的是有限样本的噪声观测，追求校准似乎难以实现。一个较弱的校准概念是自校准，它意味着给定相同均值估计的响应的期望与该估计匹配。这一概念在保险定价中尤为重要，因为它确保不同价格群体之间不存在交叉补贴。在本文中，我们展示了提升树可用于分别检验校准和自校准的必要条件。我们的方法通过一个数值例子证明了其实用相关性，其中所提出的测试在一个大型保险数据集上表现出很强的能力。
英文摘要：
The main goal in regression modelling consists in approximating the conditional mean of a response given a set of features. A regression function is said to be calibrated if the resulting mean estimates match the true conditional means for almost every set of features. Aiming for calibration seems not achievable in practice as one typically deals with finite samples of noisy observations. A weaker notion of calibration is auto-calibration, and it means that the expectation of responses being given the same mean estimate matches this estimate. This notion is important, e.g., in insurance pricing as it ensures no cross-subsidization between different price cohorts. In this paper, we show that boosting trees can be used to test necessary conditions for calibration and auto-calibration, respectively. The practical relevance of our approach is supported by a numerical example, in which the proposed tests prove to be very powerful on a large insurance dataset.
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。
