**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 |

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

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

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

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`

.

`CompactRegressionGP`

| `compact`

| `fitrgp`

| `plotPartialDependence`

- Class Attributes (MATLAB)
- Property Attributes (MATLAB)