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In high-dimensional prediction settings, it remains challenging to reliablyestimate the test performance. To address this challenge, a novel performanceestimation framework is presented. This framework, called Learn2Evaluate, isbased on learning curves by fitting a smooth monotone curve depicting testperformance as a function of the sample size. Learn2Evaluate has severaladvantages compared to commonly applied performance estimation methodologies.Firstly, a learning curve offers a graphical overview of a learner. Thisoverview assists in assessing the potential benefit of adding training samplesand it provides a more complete comparison between learners than performanceestimates at a fixed subsample size. Secondly, a learning curve facilitates inestimating the performance at the total sample size rather than a subsamplesize. Thirdly, Learn2Evaluate allows the computation of a theoreticallyjustified and useful lower confidence bound. Furthermore, this bound may betightened by performing a bias correction. The benefits of Learn2Evaluate areillustrated by a simulation study and applications to omics data.

Estimation of Predictive Performance in High-Dimensional Data Settings using Learning Curves