oobPredict

Predict out-of-bag response of ensemble

Syntax

```Yfit = oobPredict(ens) Yfit = oobPredict(ens,Name,Value) ```

Description

`Yfit = oobPredict(ens)` returns the predicted responses for the out-of-bag data in `ens`.

`Yfit = oobPredict(ens,Name,Value)` predicts responses with additional options specified by one or more `Name,Value` pair arguments.

Input Arguments

 `ens` A regression bagged ensemble, constructed with `fitrensemble`.

Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside quotes. You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

 `'learners'` Indices of weak learners in the ensemble ranging from `1` to `NumTrained`. `oobLoss` uses only these learners for calculating loss. Default: `1:NumTrained`

Output Arguments

 `Yfit` A vector of predicted responses for out-of-bag data. `Yfit` has `size(ens.X,1)` elements. You can find the indices of out-of-bag observations for weak learner `L` with the command `~ens.UseObsForLearner(:,L)`

Examples

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Compute the out-of-bag predictions for the `carsmall` data set. Display the first three terms of the fit.

Load the `carsmall` data set and select displacement, horsepower, and vehicle weight as predictors.

```load carsmall X = [Displacement Horsepower Weight];```

Train an ensemble of bagged regression trees.

`ens = fitrensemble(X,MPG,'Method','Bag');`

Find the out-of-bag predictions, and display the first three terms of the fit.

```Yfit = oobPredict(ens); Yfit(1:3) % First three terms```
```ans = 3×1 15.5200 14.5558 15.0231 ```