相关实验视频
Updated: May 25, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
对可解释风险评估建模的影响方向.
Emanuele Borgonovo1, Manel Baucells2, Antonio De Rosa1
1Bocconi University, Milan, Italy.
本研究评估了模型可解释性的图形指标,发现只有PD函数与模型属性一致一致. 分析师在选择可视化工具时还应该考虑外推风险.
科学领域:
- 定量建模 定量建模 定量建模
- 风险分析 风险分析
- 数据可视化数据可视化
背景情况:
- 图形指标有助于对决策者和风险分析师的复杂模型中的输入效应进行可视化.
- 关于各种边际效应指标的充分性和一致性的理解有限.
研究的目的:
- 调查流行的边际效应指标与定量模型属性的一致性.
- 检查指标一致性关于单调性,利普希茨和性属性.
主要方法:
- 评价受欢迎的边际效应指标.
- 评估与模型属性的一致性,如单调性,利普希茨和性.
- 考虑模型外推风险的考虑.
主要成果:
- 令人惊的是,只有默认概率 (PD) 函数与所有检查的模型属性一致.
- 当外推风险可控时,建议使用个人条件期望 (ICE) 图表.
结论:
- PD函数提供可靠的模型行为见解,满足关键一致性标准.
- 视觉化指标的选择必须平衡模型的一致性与外推风险.
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