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2005 Jan 18
1
Interpretation of randomForest results
...observed = ntrain[, "LESION"], predicted = ntrain.pred)
>
> I got the following results. It seemed that the
> classification rates for 'lesion' and 'noninf' classes are 0.
> Any suggestion will be very appreciated.
randomForest is rather good at overfitting _training_ data, but that's
(usually) not a problem in classification. What one usually cares about is
the _test set_ performance. There, randomForest performance does not
degrade as the number of trees increases, and that's what Breiman meant by
`random forests do not overfit'.
Andy
>
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