Hello list,
I'm trying to generate classifiers for a certain task using several
methods, one of them being decision trees. The doubts come when I want to
estimate the cross-validation error of the generated tree:
tree <- rpart(y~., data=data.frame(xsel, y), cp=0.00001)
ptree <- prune(tree,
cp=tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"])
ptree$cptable
CP nsplit rel error xerror xstd
1 0.33120000 0 1.0000 1.0000 0.02856022
2 0.08640000 1 0.6688 0.6704 0.02683544
3 0.02986667 2 0.5824 0.5856 0.02584564
4 0.02880000 5 0.4928 0.5760 0.02571738
5 0.01920000 6 0.4640 0.5168 0.02484761
6 0.01440000 8 0.4256 0.5056 0.02466708
7 0.00960000 12 0.3552 0.5024 0.02461452
8 0.00880000 15 0.3264 0.4944 0.02448120
9 0.00800000 17 0.3088 0.4768 0.02417800
10 0.00480000 25 0.2448 0.4672 0.02400673
If I got it right, "xerror" stands for the cross-validation error (using
10-fold by default), this is pretty high (0.4672 over 1). However, if I do
something similar using tune from e1071 I get a much lower error:
treetune <- tune(rpart, y~., data=data.frame(xsel, y), predict.func =
treeClassPrediction, cp=0.0048)
> treetune$best.performance[1] 0.2243049
I'm also assuming that the performance returned by "tune" is the
cross-validation error (also 10-fold by default). So where does this
enormous difference come from? What am I missing?
Also, "rel error" is the relative error in the training set? The
documentation is not very descriptive:
cptable- the table of optimal prunings based on a complexity parameter.
Thanks and happy pre-new year,
-- israel
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