卡罗特:通过回归进行预测建模的R包,调整为多种调整方法
Alina Bazarova1,2, Marko Raseta3
1Jülich Supercomputing Center, Forschungszentrum Jülich, Jülich, Germany.
PloS one
|October 12, 2023
概括
我们介绍CARRoT,一个用于预测建模的R包,它使用交叉验证和启发式规则来找到最佳的回归模型. 它通过考虑对线性,每变量事件规则和R平方统计数据来增强模型选择,以进行可靠的预测能力评估.
科学领域:
- 统计建模 统计建模
- 机器学习是机器学习.
- 生物信息学是一种生物信息学.
背景情况:
- 预测建模对于数据分析至关重要.
- 现有的模型选择方法有局限性.
- 强大的内部验证对于可靠的预测模型至关重要.
研究的目的:
- 介绍CARRoT,一个用于预测建模的R包.
- 为探索性数据分析和模型选择提供一个工具.
- 将CARRoT的性能与其他模型选择技术进行比较.
主要方法:
- 卡罗特对回归模型进行了详尽的搜索.
- 它在内部模型验证中采用多个保留时间.
- 考虑了对线性,每变量事件规则 (EPV),R平方和相互作用.
主要成果:
- 通过交叉验证,CARRoT根据预测能力对候选模型进行排名.
- 它使用错误,准确度和AUROC量化预测性能.
- 该包可促进并行处理,以减少计算时间.
结论:
- 卡罗特为预测模型选择提供了一种全面的方法.
- 它为评估和比较不同的建模策略提供了一个强大的框架.
- 该套件有助于识别具有卓越预测精度的模型.
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