# resubPredict

Predict response of regression ensemble by resubstitution

## Syntax

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

## Description

example

````Yfit = resubPredict(ens)` returns a vector of `ens``.X` elements containing the responses predicted by `ens` for the data `ens.X`. `Yfit` contains the predictions of `ens` on the data used by `fitrensemble` to create `ens`.```
````Yfit = resubPredict(ens,Name=Value)` specifies additional options using one or more name-value arguments. For example, you can specify the indices of weak learners for predicting responses, and whether to perform computations in parallel.```

## Examples

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Find the resubstitution predictions of mileage from the `carsmall` data, and look at their mean-squared difference from the training data.

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

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

Train an ensemble of regression trees.

`ens = fitrensemble(X,MPG,Method="LSBoost",Learners="Tree");`

Find the resubstitution predictions of `MPG`.

`Yfit = resubPredict(ens);`

Calculate the mean-squared difference of the resubstitution predictions from the training data.

`MSE = mean((Yfit - ens.Y).^2)`
```MSE = 0.5836 ```

Confirm that the result is the same as the result of `resubLoss`.

`resubLoss(ens)`
```ans = 0.5836 ```

## Input Arguments

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Regression ensemble model, specified as a `RegressionEnsemble` model object trained 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.

Example: `resubPredict(ens,Learners=[1 2 3 5],UseParallel=true)` specifies to use the first, second, third, and fifth learners in the ensemble, and to perform computations in parallel.

Indices of weak learners in the ensemble to use in `resubPredict`, 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`

Flag to run in parallel, specified as a numeric or logical `1` (`true`) or `0` (`false`). If you specify `UseParallel=true`, the `resubPredict` function executes `for`-loop iterations by using `parfor`. The loop runs in parallel when you have Parallel Computing Toolbox™.

Example: `UseParallel=true`

Data Types: `logical`

## Output Arguments

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Predicted response values, returned as a numeric column vector with the same number of rows as `X`. Each row of `Yfit` gives the predicted response to the corresponding row of `X`, based on the regression model `ens`.

## Version History

Introduced in R2011a