I'm playing with a randomForest and have found that generally increasing the sampSize leads to better performance. Is there a rule / formula / etc that suggests what the optimal sampSize should be or is it a trial and error thing? I guess another way of phrasing it; what are my risks of too small of a sampSize or too large (overfitting?)?
This question is referring to the R implementation of random forest in the
randomForest package. The function
randomForest has a parameter
sampSize which is described in the documentation as
Size(s) of sample to draw. For classification, if sampsize is a vector of the length the number of strata, then sampling is stratified by strata, and the elements of sampsize indicate the numbers to be drawn from the strata.