# treeval

Predicted responses

`treeval` will be removed in a future release. Use `fitctree` or `fitrtree` to grow a tree. Then use `predict` (`ClassificationTree`) or `predict` (`RegressionTree`) instead of `treeval`.

## Syntax

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

## Description

`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 `j`th 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.