停止标准对回归随机森林中的树木生长的影响
Aryana Arsham1, Philip Rosenberg2, Mark Little3
1Center for Data, Mathematical & Computational Sciences, Integrative Data Analytics, Goucher College, USA.
概括
这项研究引入了一种新的方法来设置随机森林中的节点大小,即机器学习技术. 拟议的方法提供了竞争性的概括错误,特别是对于具有高可变性的数据集.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 数据科学数据科学数据科学
背景情况:
- 随机森林是强大的机器学习算法,用于建模复杂的关系.
- 节点大小是一个影响随机森林性能的关键超参数.
- 现有的方法限制了父节点的大小,可能会影响预测准确度.
研究的目的:
- 为随机森林节点大小提出一个替代的超参数定义:终端节点的最小观测.
- 用模拟和现实世界的数据,将拟议的方法与原来的布雷曼方法进行比较.
- 开发一种权重方法,用于应用随机森林来调查数据.
主要方法:
- 在回归中比较两个随机森林节点大小策略.
- 基于概括错误,偏差平方和方差的评估.
- 应用到模拟数据集和来自NHANES的2型糖尿病数据.
- 开发调查数据分析的权重方法.
主要成果:
- 拟议的终端节点大小定义产生了竞争性概括错误.
- 当数据具有很大的随机错误可变性时,性能特别强.
- 新方法有效地纳入了调查数据分析的权重.
结论:
- 拟议的随机森林节点大小策略是原始方法的可行替代方案.
- 这种方法增强了复杂数据集的分析,包括调查数据.
- 开发的R代码为实施拟议方法提供了一个实用的工具.
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