Class: RegressionGP
Cross-validate Gaussian process regression model
cvMdl = crossval(gprMdl)
cvmdl = crossval(gprMdl,Name,Value)
returns the partitioned model, cvMdl
= crossval(gprMdl
)cvMdl
, built from the Gaussian process regression (GPR) model, gprMdl
, using 10-fold cross validation.
cvmdl
is a RegressionPartitionedModel
object, and gprMdl
is a RegressionGP
(full) object.
returns the partitioned model, cvmdl
= crossval(gprMdl
,Name,Value
)cvmdl
, with additional options specified by one or more Name,Value
pair arguments. For example, you can specify the number of folds or the fraction of the data to use for testing.
You can only use one of the name-value pair arguments at a time.
You cannot compute the prediction intervals for a cross-validated model.
Alternatively, you can train a cross-validated model using the related name-value pair arguments in fitrgp
.
If you supply a custom 'ActiveSet'
in the call to fitrgp
, then you cannot cross validate the GPR model.
[1] Harrison, D. and D.L., Rubinfeld. "Hedonic prices and the demand for clean air." J. Environ. Economics & Management. Vol.5, 1978, pp. 81-102.
[2] 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.
[3] 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.
[4] Lichman, M. UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science, 2013. http://archive.ics.uci.edu/ml.
fitrgp
| kfoldLoss
| kfoldPredict
| RegressionGP
| RegressionPartitionedModel