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Regression error for support vector machine regression model

`L = loss(mdl,tbl,ResponseVarName)`

L = loss(mdl,tbl,Y)

L = loss(mdl,X,Y)

L = loss(___,Name,Value)

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.

If

`mdl`

is a cross-validated`RegressionPartitionedSVM`

model, use`kfoldLoss`

instead of`loss`

to calculate the regression error.