在分类和回归树中进行数学优化
Emilio Carrizosa1, Cristina Molero-Río1, Dolores Romero Morales2
1Instituto de Matemáticas de la Universidad de Sevilla, Seville, Spain.
概括
本文回顾了分类和回归树的优化方法,提高了它们的灵活性. 新的配方改善了成本敏感性,可解释性,公平性和复杂数据的处理.
科学领域:
- 机器学习 机器学习
- 持续优化 持续优化
- 混合整数线性优化混合整数线性优化
背景情况:
- 分类和回归树是标准的机器学习工具.
- 现有的方法在灵活性上有局限性,并结合了先进的特性.
研究的目的:
- 审查基于树的模型优化方面的最新进展.
- 使用连续和混合整数线性优化探索新的配方.
- 提高树模型的灵活性和适用性.
主要方法:
- "持续优化"的最新贡献的回顾.
- 综述最近在混合整数线性优化方面的贡献.
- 基于决策变量,约束和算法的公式的比较.
主要成果:
- 新的优化配方为树模型提供了更大的灵活性.
- 这些表述有助于纳入成本敏感性,可解释性和公平性.
- 这些方法对于处理复杂数据类型,包括功能数据,是有效的.
结论:
- 优化范式为推进基于树的机器学习提供了强大的工具.
- 新的配方显著提高了分类和回归树的能力.
- 这项研究为更复杂和可解释的树模型开辟了道路.
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