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**Superclasses: **`CompactRegressionGP`

Gaussian process regression model class

`RegressionGP`

is a Gaussian process regression
(GPR) model. You can train a GPR model, using `fitrgp`

.
Using the trained model, you can

Predict responses for training data using

`resubPredict`

or new predictor data using`predict`

. You can also compute the prediction intervals.Compute the regression loss for training data using

`resubLoss`

or new data using`loss`

.

Create a `RegressionGP`

object by using `fitrgp`

.

`compact` | Create compact Gaussian process regression model |

`crossval` | Cross-validate Gaussian process regression model |

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

`loss` | Regression error for Gaussian process regression model |

`partialDependence` | Compute partial dependence |

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

`postFitStatistics` | Compute post-fit statistics for the exact Gaussian process regression model |

`predict` | Predict response of Gaussian process regression model |

`resubLoss` | Resubstitution loss for a trained Gaussian process regression model |

`resubPredict` | Resubstitution prediction from a trained Gaussian process regression model |

`shapley` | Shapley values |

You can access the properties of this class using dot notation. For example,

`KernelInformation`

is a structure holding the kernel parameters and their names. Hence, to access the kernel function parameters of the trained model`gprMdl`

, use`gprMdl.KernelInformation.KernelParameters`

.