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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