Main Content

**Superclasses: **`CompactRegressionSVM`

Support vector machine regression model

`RegressionSVM`

is a support vector machine (SVM) regression model. Train a `RegressionSVM`

model using `fitrsvm`

and the sample data.

`RegressionSVM`

models store data, parameter values, support vectors, and algorithmic implementation information. You can use these models to:

Estimate resubstitution predictions. For details, see

`resubPredict`

.Predict values for new data. For details, see

`predict`

.Compute resubstitution loss. For details, see

`resubLoss`

.Compute the mean square error or epsilon-insensitive loss. For details, see

`loss`

.

Create a `RegressionSVM`

object by using `fitrsvm`

.

`compact` | Compact support vector machine regression model |

`crossval` | Cross-validated support vector machine regression model |

`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 |

`resubLoss` | Resubstitution loss for support vector machine regression model |

`resubPredict` | Predict resubstitution response of support vector machine regression model |

`resume` | Resume training support vector machine regression model |

`shapley` | Shapley values |

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

[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.