predict
Predict response of Gaussian process regression model
Syntax
ypred = predict(gprMdl,Xnew)
[ypred,ysd,yint]
= predict(gprMdl,Xnew)
[ypred,ysd,yint]
= predict(gprMdl,Xnew,'Alpha',alpha)
Description
returns the predicted responses ypred
= predict(gprMdl
,Xnew
)ypred
for the Gaussian process
regression (GPR) model gprMdl
and the predictor values in
Xnew
.
[
also returns the standard deviations ypred
,ysd
,yint
]
= predict(gprMdl
,Xnew
)ysd
and 95% prediction
intervals yint
of the response variable, evaluated at each
observation in Xnew
using the trained GPR model.
[
specifies the significance level for the confidence level of the prediction intervals
ypred
,ysd
,yint
]
= predict(gprMdl
,Xnew
,'Alpha',alpha
)yint
. The confidence level of yint
is
equal to 100(1 – Alpha)%
.
Input Arguments
Output Arguments
Examples
Tips
You can choose the prediction method while training the GPR model using the
PredictMethod
name-value pair argument infitrgp
. 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 whenPredictMethod
is'bcd'
.If
gprMdl
is aCompactRegressionGP
object, you cannot compute standard deviations,ysd
, or prediction intervals,yint
, forPredictMethod
equal to'sr'
or'fic'
. To computeysd
andyint
forPredictMethod
equal to'sr'
or'fic'
, use the full regression (RegressionGP
) object.
Alternatives
You can use resubPredict
to compute the predicted responses for the trained GPR
model at the observations in the training data.
Extended Capabilities
Version History
See Also
fitrgp
| RegressionGP
| CompactRegressionGP
| compact
| resubPredict
| loss