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Predict responses using regression tree

predicts
response values with additional options specified by one or more `Yfit`

= predict(`Mdl`

,`X`

,`Name,Value`

)`Name,Value`

pair
arguments. For example, you can specify to prune `Mdl`

to
a particular level before predicting responses.

To integrate the prediction of a regression tree model into Simulink^{®}, you can use the RegressionTree
Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^{®} Function block with the `predict`

function. For
examples, see Predict Responses Using RegressionTree Predict Block and Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, consider the following:

If you use the Statistics and Machine Learning Toolbox library block, you can use the

**Fixed-Point Tool (Fixed-Point Designer)**to convert a floating-point model to fixed point.Support for variable-size arrays must be enabled for a MATLAB Function block with the

`predict`

function.If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

`compact`

| `CompactRegressionTree`

| `fitrtree`

| `loss`

| `RegressionTree`