# RegressionPartitionedEnsemble

Cross-validated regression ensemble

## Description

`RegressionPartitionedEnsemble`

is a set of
regression ensembles trained on cross-validated folds. Estimate the quality of classification
by cross validation using one or more “kfold” methods: `kfoldfun`

, `kfoldLoss`

, or `kfoldPredict`

. Every “kfold” method uses models trained on in-fold
observations to predict response for out-of-fold observations. For example, suppose you cross
validate using five folds. In this case, every training fold contains roughly 4/5 of the data
and every test fold contains roughly 1/5 of the data. The first model stored in
`Trained{1}`

was trained on `X`

and `Y`

with the first 1/5 excluded, the second model stored in `Trained{2}`

was
trained on `X`

and `Y`

with the second 1/5 excluded, and so
on. When you call `kfoldPredict`

, it computes predictions for the first
1/5 of the data using the first model, for the second 1/5 of data using the second model and
so on. In short, response for every observation is computed by `kfoldPredict`

using the model trained without this observation.

## Creation

You can create a `RegressionPartitionedEnsemble`

object in two ways:

Create a cross-validated model from a

`RegressionEnsemble`

or`RegressionBaggedEnsemble`

model object by using the`crossval`

object function.Create a cross-validated model by using the

`fitrensemble`

function and specifying one of the name-value arguments`CrossVal`

,`CVPartition`

,`Holdout`

,`KFold`

, or`Leaveout`

.

## Properties

## Object Functions

`gather` | Gather properties of Statistics and Machine Learning Toolbox object from GPU |

`kfoldLoss` | Loss for cross-validated partitioned regression model |

`kfoldPredict` | Predict responses for observations in cross-validated regression model |

`kfoldfun` | Cross-validate function for regression |

`resume` | Resume training of cross-validated regression ensemble model |

## Examples

## Extended Capabilities

## Version History

**Introduced in R2011a**