For K iterations, a classifier is trained and its accuracy measured by splitting the data into a test set and training set.
The number of iterations k is given by the -tries option. Default 6.
By default, the test set is a random subset size 1/K of the observations, and the training set is the remaining (K - 1)/K observations. This can be changed by the -testpct option which specifies the size of the test set as a percentage. For example, using -tries 5 -testpct 10 will perform five iterations where the test set is 1/10th of the observations.
The -tabbedout option specifies a k-fold validation tabbed output file.
usearch -forest_kfold feature_table.txt -tabbedout