Figure 1
From: CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data
Evaluation schemes. The top panel illustrates the splitting into learning and test data sets. The whole sample S is split into a learning set and a test set . The classifier f(·) is constructed using the learning set and subsequently applied to the test set . The bottom panel displays schematically k-fold cross-validation (left), Monte-Carlo cross-validation with n = 5 and ntrain = 3 (middle), and bootstrap sampling (with replacement) with n = 5 and ntrain = 3 (right).