# RegressionPartitionedLinear

Cross-validated linear regression model for high-dimensional data

## Description

`RegressionPartitionedLinear`

is a set of linear regression
models trained on cross-validated folds. You can estimate the predictive quality of the
model, or how well the linear regression model generalizes, using one or more
*kfold* functions: `kfoldPredict`

and `kfoldLoss`

.

Every *kfold* object function uses models trained on
training-fold (in-fold) observations to predict the response for validation-fold
(out-of-fold) observations. For example, suppose that you cross-validate using five
folds. The software randomly assigns each observation into five groups of equal size
(roughly). The *training fold* contains four of the groups (roughly
4/5 of the data), and the *validation fold* contains the other
group (roughly 1/5 of the data). In this case, cross-validation proceeds as follows:

The software trains the first model (stored in

`CVMdl.Trained{1}`

) by using the observations in the last four groups, and reserves the observations in the first group for validation.The software trains the second model (stored in

`CVMdl.Trained{2}`

) by using the observations in the first group and the last three groups. The software reserves the observations in the second group for validation.The software proceeds in a similar manner for the third, fourth, and fifth models.

If you validate by using `kfoldPredict`

, the software computes
predictions for the observations in group *i* by using model
*i*. In short, the software estimates a response for every
observation using the model trained without that observation.

**Note**

Unlike other cross-validated regression models,
`RegressionPartitionedLinear`

model objects do not store the
predictor data set.

## Creation

You can create a `RegressionPartitionedLinear`

object by using the
`fitrlinear`

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

,
`CVPartition`

, `Holdout`

,
`KFold`

, or `Leaveout`

.

## Properties

## Object Functions

`kfoldLoss` | Regression loss for cross-validated linear regression model |

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

## Examples

## Extended Capabilities

## Version History

**Introduced in R2016a**