loss
Regression error for support vector machine regression model
Syntax
L = loss(mdl,Tbl,ResponseVarName)
L = loss(mdl,Tbl,Y)
L = loss(mdl,X,Y)
L = loss(___,Name,Value)
Description
returns the loss for the predictions of the support vector machine (SVM) regression
model, L
= loss(mdl
,Tbl
,ResponseVarName
)mdl
, based on the predictor data in the table
Tbl
and the true response values in
Tbl.ResponseVarName
.
returns the loss for the predictions of the support vector machine (SVM) regression
model, L
= loss(mdl
,Tbl
,Y
)mdl
, based on the predictor data in the table
X
and the true response values in the vector
Y
.
returns
the loss for the predictions of the support vector machine (SVM) regression
model, L
= loss(mdl
,X
,Y
)mdl
, based on the predictor data in X
and
the true responses in Y
.
returns
the loss with additional options specified by one or more L
= loss(___,Name,Value
)Name,Value
pair
arguments, using any of the previous syntaxes. For example, you can
specify the loss function or observation weights.
Note
If the predictor data X
or the predictor variables in
Tbl
contain any missing values, the
loss
function can return NaN. For more
details, see loss can return NaN for predictor data with missing values.
Input Arguments
Output Arguments
Examples
More About
Tips
If
mdl
is a cross-validatedRegressionPartitionedSVM
model, usekfoldLoss
instead ofloss
to calculate the regression error.