# modelAccuracyPlot

Plot observed default rates compared to predicted PDs on grouped data

*Since R2021a*

`modelAccuracyPlot`

is renamed to
`modelCalibrationPlot`

. `modelAccuracyPlot`

is not
recommended. Use `modelCalibrationPlot`

instead.

## Syntax

## Description

`modelAccuracyPlot(`

plots the observed default rates compared to the predicted probabilities of default
(PD). `pdModel`

,`data`

,`GroupBy`

)`GroupBy`

is required and can be any column in the
`data`

input (not necessarily a model variable). The
`modelAccuracyPlot`

function computes the observed PD as the
default rate of each group and the predicted PD as the average PD for each group.
`modelAccuracyPlot`

supports comparison against a reference
model.

`modelAccuracyPlot(___,`

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

)

specifies options using one or more name-value pair arguments in addition to the
input arguments in the previous syntax and returns the figure handle
`h`

= modelAccuracyPlot(`ax`

,___,`Name,Value`

)`h`

.

## 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.

[3] Breeden, Joseph.
*Living with CECL: The Modeling Dictionary.* Santa Fe, NM:
Prescient Models LLC, 2018.

[4] Roesch, Daniel and Harald
Scheule. *Deep Credit Risk: Machine Learning with Python.*
Independently published, 2020.

## Version History

**Introduced in R2021a**

## See Also

`modelDiscrimination`

| `modelDiscriminationPlot`

| `modelAccuracy`

| `predictLifetime`

| `predict`

| `fitLifetimePDModel`

| `Logistic`

| `Probit`

| `Cox`

| `customLifetimePDModel`

### Topics

- Basic Lifetime PD Model Validation
- Compare Logistic Model for Lifetime PD to Champion Model
- Compare Lifetime PD Models Using Cross-Validation
- Expected Credit Loss Computation
- Compare Model Discrimination and Model Calibration to Validate of Probability of Default
- Compare Probability of Default Using Through-the-Cycle and Point-in-Time Models
- Overview of Lifetime Probability of Default Models