Compact Gaussian process regression model class
CompactRegressionGP is a compact Gaussian process regression (GPR)
model. The compact model consumes less memory than a full model, because it does not
include the data used for training the GPR model.
Because the compact model does not include the training data, you cannot perform some
tasks, such as cross-validation, using the compact model. However, you can use the
compact model for making predictions or calculate regression loss for new data (use
gprMdl— Full, trained Gaussian process regression model
Full, trained Gaussian process regression model, specified as a
RegressionGP model, returned by
|loss||Regression error for Gaussian process regression model|
|predict||Predict response of Gaussian process regression model|
Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).
Usage notes and limitations:
predict function supports code
When you train a Gaussian process regression model by using
fitrgp, you cannot supply training data in a table that contains a
logical vector, character array, categorical array, string array, or cell array of
character vectors. Also, you cannot use the
'CategoricalPredictors' name-value pair argument. Code generation
does not support categorical predictors. To
include categorical predictors in a model, preprocess the categorical predictors
dummyvar before fitting the
For more information, see Introduction to Code Generation.