# idTreeEnsemble

Decision tree ensemble mapping function for nonlinear ARX models (requires Statistics and Machine Learning Toolbox)

## Description

An `idTreeEnsemble`

object implements a decision tree ensemble
model, and is a nonlinear mapping function for estimating nonlinear ARX models. This mapping
object incorporates regression tree ensembles that the mapping function creates using
Statistics and Machine Learning Toolbox™. Unlike most other mapping objects for `idnlarx`

models, which typically contain offset, linear, and nonlinear components,
the `idTreeEnsemble`

model contains only a nonlinear component.

Mathematically, the `idTreeEnsemble`

object maps *m*
inputs *x*(*t*) =
[*x*_{1}(*t*),*x*_{2}(*t*),…,*x _{m}*(

*t*)]

^{T}to a scalar output

*y*(

*t*) using a decision tree regression ensemble model.

Here:

*x*(*t*) is an*m*-by-1 vector of inputs, or*regressors*.*y*(*t*) is the scalar output.

For more information about creating regression tree ensembles, see `fitrensemble`

(Statistics and Machine Learning Toolbox).

Use `idTreeEnsemble`

as the value of the `OutputFcn`

property of an `idnlarx`

model. For example, specify
`idTreeEnsemble`

when you estimate an `idnlarx`

model with the
following
command.

sys = nlarx(data,regressors,idTreeEnsemble)

`nlarx`

estimates the model, it essentially estimates the parameters of the
`idTreeEnsemble`

object.
You can configure the `idTreeEnsemble`

function to set options and fix
parameters. To modify the estimation options, set the option property in
`E.EstimationOptions`

, where `E`

is the
`idTreeEnsemble`

object. For example, to change the fit method to
`'lsboost-resampled'`

, use ```
E.EstimationOptions.FitMethod =
'lsboost-resampled'
```

. To fix the values of an existing estimated
`idTreeEnsemble`

during subsequent `nlarx`

estimations,
set the `Free`

property to `false`

. To apply parallel
processing, set `E.EstimationOptions.UseParallel`

to `true`

.
Use `evaluate`

to compute the output of the function for a given vector of regressor
inputs.

## Creation

### Description

creates an empty
`E`

= idTreeEnsemble`idTreeEnsemble`

object `E`

with the default
estimation fit method of `'bag'`

. The number of regressor inputs is
determined during model estimation and the number of `idTreeEnsemble`

outputs is 1.

sets the ensemble estimation method to the value in `E`

= idTreeEnsemble(`fitmethod`

)`fitmethod`

.

### Input Arguments

## Properties

## Examples

## Extended Capabilities

## Version History

**Introduced in R2021b**

## See Also

`nlarx`

| `idnlarx`

| `fitrensemble`

(Statistics and Machine Learning Toolbox) | `evaluate`

### Topics

- Framework for Ensemble Learning (Statistics and Machine Learning Toolbox)
- Available Mapping Functions for Nonlinear ARX Models