论文标题
使用学习曲线估算高维数据设置中的预测性能
Estimation of Predictive Performance in High-Dimensional Data Settings using Learning Curves
论文作者
论文摘要
在高维预测设置中，可靠地估计测试性能仍然具有挑战性。为了应对这一挑战，提出了一个新颖的性能估计框架。该框架称为Learn2Evaluate，是基于学习曲线的，它通过拟合平滑的单调曲线将测试性能描绘为样本量的函数。与常用的性能估计方法相比，Learn2 verauate具有多个优势。首先，学习曲线提供了学习者的图形概述。该概述有助于评估添加培训样本的潜在优势，并且比固定子样本大小的绩效估计值更完整地比较学习者。其次，学习曲线有助于估算总样本量而不是子样本大小的性能。第三，Learn2Aluate允许计算理论上是合理且有用的较低置信度结合。此外，可以通过执行偏置校正来拧紧这种结合。通过模拟研究和对OMICS数据的应用来说明LEAL2评论的好处。
In high-dimensional prediction settings, it remains challenging to reliably estimate the test performance. To address this challenge, a novel performance estimation framework is presented. This framework, called Learn2Evaluate, is based on learning curves by fitting a smooth monotone curve depicting test performance as a function of the sample size. Learn2Evaluate has several advantages compared to commonly applied performance estimation methodologies. Firstly, a learning curve offers a graphical overview of a learner. This overview assists in assessing the potential benefit of adding training samples and it provides a more complete comparison between learners than performance estimates at a fixed subsample size. Secondly, a learning curve facilitates in estimating the performance at the total sample size rather than a subsample size. Thirdly, Learn2Evaluate allows the computation of a theoretically justified and useful lower confidence bound. Furthermore, this bound may be tightened by performing a bias correction. The benefits of Learn2Evaluate are illustrated by a simulation study and applications to omics data.
