Figure 1
From: Improving peptide-MHC class I binding prediction for unbalanced datasets
![Figure 1](http://media.springernature.com/full/springer-static/image/art%3A10.1186%2F1471-2105-9-385/MediaObjects/12859_2008_Article_2370_Fig1_HTML.jpg)
Theoretical relation between λ 2 and EK ( θ ). Theoretical relationship between the training false negative cost (λ2) that minimizes the expected cost of a classifier (EK(θ)) for a given type 2 error cost (κ2). The dotted lines represent one standard deviation from the mean. Here κ1 = 1, λ1 = 1 and π = 0.5.