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forest_kfold command

See also
  random forest classifiers
  forest_train command
  forest_classify command

Performs k-fold cross-validation of random forest classifiers on a feature table with known categories.

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 results.txt