相关实验视频
Updated: Jul 12, 2025

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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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通过基于meta-learned交叉验证的符合性预测对集预测器进行短暂校准.
IEEE transactions on pattern analysis and machine intelligence
|October 24, 2023
概括
这项研究介绍了meta-XB,一种用于符合性预测 (CP) 的新型meta-learning方法. 通过使用交叉验证和自适应分数,Meta-XB增强了模型校准,并减少了预测集大小,即使数据有限.
科学领域:
- 机器学习 机器学习
- 统计学学习理论
- 不确定性定量化 不确定性定量化
背景情况:
- 频率主义模型往往缺乏可靠的不确定性量化和校准.
- 贝叶斯方法提供校准,但需要严格的模型假设.
- 合规预测 (CP) 提供了一般的校准保证,但可以产生大量的,无信息的集与有限的数据.
研究的目的:
- 引入一个新的meta-learning方案,meta-XB,用于减少规范预测中的预测集的大小.
- 提高集合预测的信息性,同时保持正式的校准保证.
- 通过适应性非符合性分数来增强每输入校准.
主要方法:
- 开发了meta-XB,这是一个基于交叉验证符合性预测 (CVCP) 的meta-learning方法.
- 保证的meta-XB保留了每项任务的正式校准保证,与以前的方法不同.
- 扩展的meta-XB与适应性不合规得分,以改善校准.
主要成果:
- 与标准CP相比,Meta-XB有效地减少了预测集的大小.
- 拟议的方法保持了每项任务的正式校准保证.
- 适应性非符合性得分在经验上改善了每输入边际校准.
结论:
- Meta-XB提供了一种通用和有效的解决方案,用于在有限的数据中增强符合性预测.
- 该方法平衡了预测集信息性与严格的校准保证.
- 通过适应性评分机制,可以通过校准实现进一步的改进.
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