Note: treeval will be removed in a future release. Use classregtree.eval instead.
yfit = treeval(t,X)
yfit = treeval(t,X,subtrees)
[yfit,node] = treeval(...)
[yfit,node,cname] = treeval(...)
yfit = treeval(t,X) takes a classification or regression tree t as produced by the treefit function and a matrix X of predictor values, and produces a vector yfit of predicted response values. For a regression tree, yfit(i) is the fitted response value for a point having the predictor values X(i,:). For a classification tree, yfit(i) is the class number into which the tree would assign the point with data X(i,:). To convert the number into a class name, use the third output argument, cname (described below).
yfit = treeval(t,X,subtrees) takes an additional vector subtrees of pruning levels, with 0 representing the full, unpruned tree. T must include a pruning sequence as created by the treefit or prunetree function. If subtree has k elements and X has n rows, the output yfit is an n-by-k matrix, with the jth column containing the fitted values produced by the subtrees(j) subtree. subtrees must be sorted in ascending order.
[yfit,node] = treeval(...) also returns an array node of the same size as yfit containing the node number assigned to each row of X. The treedisp function can display the node numbers for any node you select.
Find the predicted classifications for Fisher's iris data:
load fisheriris; t = treefit(meas,species); % Create decision tree sfit = treeval(t,meas); % Find assigned class numbers sfit = t.classname(sfit); % Get class names mean(strcmp(sfit,species)) % Proportion in correct class ans = 0.9800
 Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Boca Raton, FL: CRC Press, 1984.