对组合数据进行监督学习的三种方法,具有对式对比率
Germà Coenders1, Michael Greenacre2
1Department of Economics, Universitat de Girona, Girona, Spain.
本研究介绍了三个阶段性监督学习方法,用于在组成数据分析中选择对式逻辑. 这些方法提高了通用线性模型的预测准确性,有助于复杂数据集的解释.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
背景情况:
- 组合数据分析 (CoDa) 通常涉及高维数据.
- 配对对积分是解释CoDa的关键,但选择是具有挑战性的许多部分.
- 一般化线性模型 (GLMs) 经常用于分析此类数据.
研究的目的:
- 开发和介绍三种新的逐步监督学习方法,用于选择最佳的双相对数.
- 提高GLMs在CoDa.Da中的解释性和预测准确度.
- 提供灵活的建模策略,以适应先前的知识和各种停止标准.
主要方法:
- 有三个阶段性的监督学习方法来选择逻辑系数:不受限制的搜索,受限制的搜索 (独特的部分) 和添加式逻辑系数.
- 将分数或共变量集成到GLM中,并有强制包含的选项.
- 应用信息标准或邦费罗尼校正的统计显著性用于模型选择停止规则.
主要成果:
- 不受限制的搜索方法产生了最高的预测准确性,尽管解释可能是复杂的.
- 限制性搜索方法提供了更直观的解释性,通过确保在logratios中独特的部分使用.
- 添加式积分法方便了对子组合的分析.
结论:
- 拟议的方法提供了有效的策略,用于为GLMs选择CoDa中的信息逻辑.
- 方法的选择取决于预测性能和可解释性之间的平衡.
- 该应用程序在现实世界生物医学研究 (克罗恩病预测) 中展示了这些方法的实用性.
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