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 |
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 |
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
.