# resubPredict

Class: RegressionEnsemble

Predict response of ensemble by resubstitution

## Syntax

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

## Description

`Yfit = resubPredict(ens)` returns the response `ens` predicts for the data `ens.X`. `Yfit` is the predictions of `ens` on the data that `fitensemble` used to create `ens`.

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

## Input Arguments

 `ens` A regression ensemble created with `fitensemble`.

### 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 single 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 to the training data, with `ens``.X` elements.

## Examples

Find the resubstitution predictions of mileage from the `carsmall` data based on horsepower and weight, and look at their mean square difference from the training data.

```load carsmall X = [Horsepower Weight]; ens = fitensemble(X,MPG,'LSBoost',100,'Tree'); Yfit = resubPredict(ens); MSE = mean((Yfit - ens.Y).^2) MSE = 6.4336```

This is the same as the result of `resubLoss`:

```resubLoss(ens) ans = 6.4336```