Figure 2 - available via license: Creative Commons Attribution 4.0 International
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Boxplots of the empirical distribution of distances of the lower 95% confidence bounds to the true AU C values (y-axis) for the bootstrap, and Learn2Evaluate without (L2E -BC) and with bias correction (L2E + BC) in the N = 100, ν = 1000 setting. The learning curve is fitted by an inverse power law and we determine n opt by MSE minimization. Outliers are not depicted.
Source publication
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...
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