Figure 3
From: Optimally splitting cases for training and testing high dimensional classifiers
Comparing two rules of thumb. Comparison of two common rules-of-thumb: 1/2 the samples to the training set and 2/3 rds of the samples to the training set. X-axis is the average accuracy (%) for training sets of size n. "Excess error" on the y-axis is the difference between the root mean squared error (RMSE) and the optimal RMSE. Each point corresponds to a cell in Table 1. Gray shading indicates scenarios where mean accuracy for full dataset size is below 60%.