# modelAccuracy

Compute R-square, RMSE, correlation, and sample mean error of predicted and observed LGDs

*Since R2021a*

`modelAccuracy`

is renamed to `modelCalibration`

.
`modelAccuracy`

is not recommended. Use `modelCalibration`

instead.

## Description

computes the R-square, root mean square error (RMSE), correlation, and sample mean
error of observed vs. predicted loss given default (LGD) data.
`AccMeasure`

= modelAccuracy(`lgdModel`

,`data`

)`modelAccuracy`

supports comparison against a reference model
and also supports different correlation types. By default,
`modelAccuracy`

computes the metrics in the LGD scale. You can
use the `ModelLevel`

name-value pair argument to compute metrics
using the underlying model's transformed scale.

`[`

specifies options using one or more name-value pair arguments in addition to the
input arguments in the previous syntax.`AccMeasure`

,`AccData`

] = modelAccuracy(___,`Name,Value`

)

## Input Arguments

## Output Arguments

## More About

## References

[1] Baesens, Bart, Daniel Roesch,
and Harald Scheule. *Credit Risk Analytics: Measurement Techniques,
Applications, and Examples in SAS.* Wiley, 2016.

[2] Bellini, Tiziano.
*IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide
with Examples Worked in R and SAS.* San Diego, CA: Elsevier,
2019.

## Version History

**Introduced in R2021a**

## See Also

`Tobit`

| `Regression`

| `Beta`

| `modelAccuracyPlot`

| `modelDiscriminationPlot`

| `modelDiscrimination`

| `predict`

| `fitLGDModel`