Greetings, I am using cforest to predict age of fishes using several variables; as it is rather difficult to age fishes I would like to show that a small subset of fish (training dataset) can be aged, then using RF analysis, age can accurately be predicted to the remaining individuals not in the subsample. In cforest_unbiased the samples are drawn without replacement and so it creates a default testing dataset (approx 35%) and training dataset from the rest. My question is that if I have already separated my data into a testing and training dataset prior to RF analysis is there a reason I should not set the fraction option to .99 to essentially eliminate the testing dataset and ensure I have the desired # of samples in my testing dataset? The resulting analysis will be applied to my specified testing dataset to assess misclassification and accuracy. I am aware that the options of cforest_unbiased do not allow allow the fraction value to be altered so my second question is is data.controls <- cforest_control(teststat = "quad", testtype = "Univ", replace = FALSE, ntree=2000, mtry=2, fraction=.99) the equivalent to data.controls <- cforest_unbiased(ntree=2000, mtry=2) except I have eliminated the default testing dataset? Thank you for your help
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