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r = risk(t)
r = risk(t,nodes)
r = risk(t) returns an n-element vector r of the risk of the nodes in the tree t, where n is the number of nodes. The risk r(i) for node i is the node error e(i) (computed by nodeerr) weighted by the node probability p(i) (computed by nodeprob).
r = risk(t,nodes) takes a vector nodes of node numbers and returns the risk values for the specified nodes.
Create a classification tree for Fisher's iris data:
load fisheriris;
t = classregtree(meas,species,...
'names',{'SL' 'SW' 'PL' 'PW'})
t =
Decision tree for classification
1 if PL<2.45 then node 2 else node 3
2 class = setosa
3 if PW<1.75 then node 4 else node 5
4 if PL<4.95 then node 6 else node 7
5 class = virginica
6 if PW<1.65 then node 8 else node 9
7 class = virginica
8 class = versicolor
9 class = virginica
view(t)

e = nodeerr(t);
p = nodeprob(t);
r = risk(t);
r
r =
0.6667
0
0.3333
0.0333
0.0067
0.0067
0.0133
0
0
e.*p
ans =
0.6667
0
0.3333
0.0333
0.0067
0.0067
0.0133
0
0[1] Breiman, L., et al., Classification and Regression Trees, Chapman & Hall, Boca Raton, 1993.
classregtree, nodeerr, nodeprob
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