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all1
2006 Jul 26
0
randomForest question [Broadcast]
...atures 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: 0.1...