Hello,

I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases.

I've seen that although there are only 32 explanatory variables the best classification performance is reached when choosing mtry=80. How is it possible that more variables can used than there are in columns the data frame?

	thanks for your help
	+ kind regards,

	Arne




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