Hello,
I've a question regarding randomForest (from the package with same name). I've
16 featurs (nominative), 159 positive and 318 negative cases that I'd like to
classify (binary classification).
Using the tuning from the e1071 package it turns out that the best performance
if reached when using all 16 features per tree (mtry=16). However, the
documentation of randomForest suggests to take the sqrt(#features), i.e. 4. How
can I explain this difference? When using all features this is the same as a
classical decision tree, with the difference that the tree is built and tested
with different data sets, right?
example (I've tried different configurations, incl. changing ntree):
> param <- try(tune(randomForest, class ~ ., data=d.all318,
> range=list(mtry=c(4, 8, 16), ntree=c(1000))));
>
> summary(param)
Parameter tuning of `randomForest':
- sampling method: 10-fold cross validation
- best parameters:
mtry ntree
16 1000
- best performance: 0.1571809
- Detailed performance results:
mtry ntree error
1 4 1000 0.1928635
2 8 1000 0.1634752
3 16 1000 0.1571809
thanks a lot for your help,
kind regards,
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