Please note that since the method random forest is chosen, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. This is explained as:
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+"In random forests, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. It is estimated internally, during the run, as follows:
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+Each tree is constructed using a different bootstrap sample from the original data. About one-third of the cases are left out of the bootstrap sample and not used in the construction of the kth tree.
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+Put each case left out in the construction of the kth tree down the kth tree to get a classification. In this way, a test set classification is obtained for each case in about one-third of the trees. At the end of the run, take j to be the class that got most of the votes every time case n was oob. The proportion of times that j is not equal to the true class of n averaged over all cases is the oob error estimate. This has proven to be unbiased in many tests."
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+The reader can find more information at: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#ooberr
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