# CompactRegressionSVM

**Package: **classreg.learning.regr

Compact support vector machine regression model

## Description

`CompactRegressionSVM`

is a compact support vector machine (SVM) regression model. It consumes less memory than a full, trained support vector machine model (`RegressionSVM`

model) because it does not store the data used to train the model.

Because the compact model does not store the training data, you cannot use it to perform certain tasks, such as cross validation. However, you can use a compact SVM regression model to predict responses using new input data.

## Construction

returns a compact SVM regression model `compactMdl`

= compact(`mdl`

)`compactMdl`

from a full, trained SVM regression model, `mdl`

. For more information, see `compact`

.

### Input Arguments

## Properties

## Object Functions

`discardSupportVectors` | Discard support vectors |

`incrementalLearner` | Convert support vector machine (SVM) regression model to incremental learner |

`lime` | Local interpretable model-agnostic explanations (LIME) |

`loss` | Regression error for support vector machine regression model |

`partialDependence` | Compute partial dependence |

`plotPartialDependence` | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |

`predict` | Predict responses using support vector machine regression model |

`shapley` | Shapley values |

`update` | Update model parameters for code generation |

## Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects.

## Examples

## 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

## See Also

`fitrsvm`

| `RegressionSVM`

| `compact`

| `update`

**Introduced in R2015b**