Fig. 7
From: Maximizing efficiency in sunflower breeding through historical data optimization
Trade-off between the average additive relationship between the training and test sets (vertical axis) and the opposite value of the average additive relationship within the training set (indicating training set diversity in horizontal axis). The values in both axes have been normalized between 0 and 1. Each point corresponds to a different method for optimizing training set composition using the optimal training set size found by Min_GRM (with the exception of Tails_GEGVs_sd1, which simultaneously optimizes training set size and composition). The values obtained for each method correspond to the average across all scenarios. For visualization purposes, the content within the large ellipse is a zoom-in of the small ellipse. The position of the optimization methods within the small ellipse is their true location