Preface to First Edition - lib

ANALYSIS USING R 169R> plot(as.party(glaucoma_prune), tp_args = list(id = FALSE))1varg< 0.209 >= 0.2093mhcg>= 0.1695 < 0.1695normal glaucoman = 7610.80.60.40.20normal glaucoman = 710.80.60.40.20normal glaucoman = 11310.80.60.40.20Figure 9.4Pruned classification tree of the glaucoma data with class distributionin the leaves.ciated with loss of optic nerves (varg smaller than 0.209 mm 3 ) indicates aglaucomateous change.As we discussed earlier, the choice of the appropriatly sized tree is not atrivial problem. For the glaucoma data, the above choice of three leaves isvery unstable across multiple runs of cross-validation. As an illustration ofthis problem we repeat the very same analysis as shown above and record theoptimal number of splits as suggested by the cross-validation runs.R> nsplitopt for (i in 1:length(nsplitopt)) {+ cp