基于集群的随机森林可视化和解释
IEEE transactions on visualization and computer graphics
|November 28, 2025
概括
这项研究引入了一种新的可视化方法,以提高随机森林的解释性,即机器学习技术. 通过对类似的决策树进行集群,用户可以理解模型性能,而无需单独分析每个树.
科学领域:
- 机器学习 机器学习
- 数据可视化 数据可视化
- 人工智能的人工智能
背景情况:
- 随机森林是强大的机器学习模型用于分类,但由于其复杂的结构,其解释性较低.
- 在随机森林中解释个别决策树通常是无法理解整体模型行为的.
研究的目的:
- 开发一种新的可视化系统和方法,以提高随机森林的解释性.
- 为了使用户能够掌握随机森林模型的一般性能,而不必检查每个决策树.
主要方法:
- 引入了一个新的距离度量来对决策树进行聚类,同时考虑决策规则和预测.
- 开发了两个可视化技术:特征图 (可视化特征拓) 和规则图 (可视化决策规则).
- 通过使用"Glass"数据集和用户研究来评估该方法.
主要成果:
- 提出的集群和可视化方法有效地代表了随机森林的集体行为.
- 用户可以通过可视化集群和单个树结构获得对模型性能的见解.
- 新的距离度量有意义地将类似的决策树组合在一起.
结论:
- 开发的可视化系统显著提高了随机森林的解释性.
- 将类似的决策树集群为理解复杂的机器学习模型提供了可扩展的方法.
- 功能图和规则图为分析决策树结构和规则提供了有价值的工具.
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