# RegressionPartitionedSVM

**Package: **classreg.learning.partition

**Superclasses: **`RegressionPartitionedModel`

Cross-validated support vector machine regression model

## Description

`RegressionPartitionedSVM`

is a set of support vector machine (SVM) regression models trained on cross-validated folds.

## Construction

returns a cross-validated (partitioned) support vector machine regression model, `CVMdl`

= crossval(`mdl`

)`CVMdl`

, from a trained SVM regression model, `mdl`

.

returns a cross-validated model with additional options specified by one or more `CVMdl`

= crossval(`mdl`

,`Name,Value`

)`Name,Value`

pair arguments. `Name`

can also be a property name and `Value`

is the corresponding value. `Name`

must appear inside single quotes (`''`

). You can specify several name-value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`

.

### Input Arguments

## 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 |

## Examples

## Alternatives

You can create a `RegressionPartitionedSVM`

model using the following techniques:

Use the training function

`fitrsvm`

and specify one of the`'CrossVal'`

,`'Holdout'`

,`'KFold'`

, or`'Leaveout'`

name-value pairs.Train a model using

`fitrsvm`

, then cross validate the model using the`crossval`

method.Create a cross validation partition using

`cvpartition`

, then pass the resulting partition object to`fitrsvm`

during training using the`'CVPartition'`

name-value pair.

## References

[1] Nash, W.J., T. L. Sellers, S. R. Talbot, A. J. Cawthorn, and W. B. Ford. "The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (*H. rubra*) from the North Coast and Islands of Bass Strait." Sea Fisheries Division, Technical Report No. 48, 1994.

[2] Waugh, S. "Extending and Benchmarking Cascade-Correlation: Extensions to the Cascade-Correlation Architecture and Benchmarking of Feed-forward Supervised Artificial Neural Networks." *University of Tasmania Department of Computer Science thesis*, 1995.

[3] Clark, D., Z. Schreter, A. Adams. "A Quantitative Comparison of Dystal and Backpropagation." submitted to the Australian Conference on Neural Networks, 1996.

[4] Lichman, M. *UCI Machine Learning Repository*, [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

## Extended Capabilities

## Version History

**Introduced in R2015b**