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treeval

Predicted responses

    Note:   treeval will be removed in a future release. Use classregtree.eval instead.

Syntax

yfit = treeval(t,X)
yfit = treeval(t,X,subtrees)
[yfit,node] = treeval(...)
[yfit,node,cname] = treeval(...)

Description

    Note:   This function is superseded by the eval method of the classregtree class and is maintained only for backwards compatibility. It accepts objects t created with the classregtree constructor.

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.

[yfit,node,cname] = treeval(...) is valid only for classification trees. It returns a cell array cname containing the predicted class names.

Examples

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

References

[1] Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Boca Raton, FL: CRC Press, 1984.

See Also

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