# oobLoss

Out-of-bag regression error

## Syntax

```L = oobLoss(ens) L = oobLoss(ens,Name,Value) ```

## Description

`L = oobLoss(ens)` returns the mean squared error for `ens` computed for out-of-bag data.

`L = oobLoss(ens,Name,Value)` computes error with additional options specified by one or more `Name,Value` pair arguments. You can specify several name-value pair arguments in any order as `Name1,Value1,…,NameN,ValueN`.

## Input Arguments

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

### Name-Value Arguments

Specify optional pairs of arguments as `Name1=Value1,...,NameN=ValueN`, where `Name` is the argument name and `Value` is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose `Name` in quotes.

Indices of weak learners in the ensemble to use in `oobLoss`, specified as a vector of positive integers in the range [1:`ens.NumTrained`]. By default, all learners are used.

Example: `Learners=[1 2 4]`

Data Types: `single` | `double`

Function handle for loss function, or `'mse'`, meaning mean squared error. If you pass a function handle `fun`, `oobLoss` calls it as

`FUN(Y,Yfit,W)`

where `Y`, `Yfit`, and `W` are numeric vectors of the same length. `Y` is the observed response, `Yfit` is the predicted response, and `W` is the observation weights.

Character vector or string scalar representing the meaning of the output `L`:

• `'ensemble'``L` is a scalar value, the loss for the entire ensemble.

• `'individual'``L` is a vector with one element per trained learner.

• `'cumulative'``L` is a vector in which element `J` is obtained by using learners `1:J` from the input list of learners.

Indication to perform inference in parallel, specified as `false` (compute serially) or `true` (compute in parallel). Parallel computation requires Parallel Computing Toolbox™. Parallel inference can be faster than serial inference, especially for large datasets. Parallel computation is supported only for tree learners.

## Output Arguments

 `L` Mean squared error of the out-of-bag observations, a scalar. `L` can be a vector, or can represent a different quantity, depending on the name-value settings.

## Examples

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Compute the out-of-bag error for the `carsmall` data.

Load the `carsmall` data set and select engine 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 error.

`L = oobLoss(ens)`
```L = 16.9551 ```