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**Class: **RegressionGP

Resubstitution prediction from a trained Gaussian process regression model

`ypred = resubPredict(gprMdl)`

[ypred,ysd]
= resubPredict(gprMdl)

[ypred,ysd,yint]
= predict(gprMdl)

[ypred,ysd,yint]
= predict(gprMdl,Name,Value)

returns
the predicted responses, `ypred`

= resubPredict(`gprMdl`

)`ypred`

, for the trained
Gaussian process regression (GPR) model, `gprMdl`

.

`[`

also returns
the estimated standard deviations of the predicted responses corresponding
to the rows of `ypred`

,`ysd`

]
= resubPredict(`gprMdl`

)`gprMdl.X`

.

`[`

also returns the
95% prediction intervals, `ypred`

,`ysd`

,`yint`

]
= predict(`gprMdl`

)`yint`

, for the true
responses corresponding to each row of training data, `gprMdl.X`

.

`[`

returns
the prediction intervals with additional options, specified by one
or more `ypred`

,`ysd`

,`yint`

]
= predict(`gprMdl`

,`Name,Value`

)`Name,Value`

pair arguments. For example,
you can specify the confidence level of the prediction interval.

You can choose the prediction method while training the GPR model using the

`PredictMethod`

name-value pair argument in`fitrgp`

. The default prediction method is`'exact'`

for*n*≤ 10000, where*n*is the number of observations in the training data, and`'bcd'`

(block coordinate descent), otherwise.Computation of standard deviations,

`ysd`

, and prediction intervals,`yint`

, is not supported when`PredictMethod`

is`'bcd'`

.

To compute the predicted responses for new data, use `predict`

.

[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] 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`

| `predict`

| `RegressionGP`

| `resubLoss`