# loss

Regression error for regression ensemble model

## Syntax

## Description

returns the mean squared error `L`

= loss(`ens`

,`tbl`

,`ResponseVarName`

)`L`

between the predictions of
`ens`

to the data in `tbl`

, compared to the
true responses `tbl.ResponseVarName`

.

The formula for `loss`

is described in the section Weighted Mean Squared Error.

returns the Classification Loss
`L`

= loss(`ens`

,`tbl`

,`ResponseVarName`

)`L`

for the trained classification ensemble model
`ens`

using the predictor data in table
`tbl`

and the true class labels in
`tbl.ResponseVarName`

. The interpretation of
`L`

depends on the loss function
(`LossFun`

) and weighting scheme
(`Weights`

). In general, better classifiers yield smaller
classification loss values. The default `LossFun`

value is
`"classiferror"`

(misclassification rate in decimal).

specifies options using one or more name-value arguments in addition to any of the
input argument combinations in the previous syntaxes. For example, you can specify
the loss function, the aggregation level for output, and whether to perform
calculations in parallel.`L`

= loss(___,`Name=Value`

)

## Input Arguments

## Examples

## More About

## Extended Capabilities

## Version History

**Introduced in R2011a**

## See Also

`predict`

| `fitrensemble`

| `RegressionEnsemble`

| `CompactRegressionEnsemble`