Amy, et al,
I agree with you and the group that comparing test set classification
errors between the two methods is the way to go.
On interpretation, I find the partial dependence plots from
randomForest are useful - especially when talking to clients about
what the forest means. See slides 32
Amy,
I have also had this issue with randomForest, that is, you lose the
ability to explain the classifier in a simple way to
non-specialists (everyone can understand the single decision tree.)
As far as comparing the accuracy of the two, I think that you are
correct in comparing them by the
Hi,
I have done an analysis using 'rpart' to construct a Classification Tree. I
am wanting to retain the output in tree form so that it is easily
interpretable. However, I am wanting to compare the 'accuracy' of the tree
to a Random Forest to estimate how much predictive ability is lost by using
Amy,
If I were you, I will check the misclassification rates in both
training set and testing set from 2 models.
On 1/28/07, Amy Koch [EMAIL PROTECTED] wrote:
Hi,
I have done an analysis using 'rpart' to construct a Classification Tree. I
am wanting to retain the output in tree form so that
