树值:回归树的选择性推理
Anna C Neufeld1, Lucy L Gao2, Daniela M Witten3
1Department of Statistics, University of Washington, Seattle, WA 98195, USA.
概括
本研究引入了一个新的选择性推断框架,用于分类和回归树 (CART). 这些方法确保可靠的统计保证,用于推断CART模型输出,控制错误率和覆盖范围.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 对分类和回归树的推理 (CART) 需要专门的方法.
- 纯粹的推断方法无法提供像1型错误控制这样的标准统计保证.
研究的目的:
- 为适配的CART模型开发一个选择性推理框架.
- 确保对CART输出进行有效的统计推断,包括错误率控制和覆盖范围.
主要方法:
- 通过对用于树木估计的数据进行条件化,提出一个选择性推断框架.
- 开发一种测试,用于控制选择性1型错误率的终端节点之间的平均响应差异.
- 在终端节点内创建平均响应的置信区间,实现名义选择性覆盖.
- 提供有效的算法来计算必要的条件集.
主要成果:
- 拟议的框架成功地控制了假设测试的选择性1型错误率.
- 置信区间实现了终端节点内的平均响应的标称选择性覆盖.
- 方法通过模拟研究和对现实世界数据集的应用来验证.
结论:
- 选择性推断框架为分析CART模型提供了一个统计学上合理的方法.
- 这些方法在机器学习算法中提高了统计推理的可靠性.
- 该方法适用于各种数据集,包括健康和营养研究中的数据集.
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