[R] CART vs. Random Forest

[Andrew Baek]

Of course, the CART & RF are different method. But at least,
I have to consider that false negative is more serious than
false positive in my problem. For this purpose, I used "prior"
in rpart and "classwt" in RF. Then, should I modify priors and 
cut-off point at the same time? 

Andrew

On Thu, 26 Sep 2002, Wiener, Matthew wrote:

> We haven't implemented different voting thresholds in the package itself,
> but when you predict you can get out votes or probabilities rather than
> classes if you want.  The argument type to predict.randomForest is "class"
> by default, but can also be "vote" or "prob".  You can use the training set
> to figure out what a good threshold is, and then check your results on a
> test set.  Then you just use the threshold later.  
> 
> I suppose we could implement a threshold that could be supplied to predict,
> but then we'd have to work something out for multi-class problems -- several
> different cutpoints, I guess.  It's not a priority for Andy or me right now.
> I actually like to take a look at the ROC curve anyway, to decide what
> tradeoffs are worthwhile.
> 
> I'd compare the results by looking at the error rates -- if you can make the
> (possibly weighted) error rate lower with one method or the other, that's
> the method that ones.
> 
> Regards,
> 
> Matt

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