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Compute RMSE of predicted and observed PDs on grouped data

computes the root mean squared error (RMSE) of the observed compared to the
predicted probabilities of default (PD). `AccMeasure`

= modelAccuracy(`pdModel`

,`data`

,`GroupBy`

)`GroupBy`

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

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

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

supports
comparison against a reference model.

`[`

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`

)

[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.

`fitLifetimePDModel`

| `Logistic`

| `modelAccuracyPlot`

| `modelDiscrimination`

| `modelDiscriminationPlot`

| `predict`

| `predictLifetime`

| `Probit`

- Basic Lifetime PD Model Validation
- Compare Logistic Model for Lifetime PD to Champion Model
- Compare Lifetime PD Models Using Cross-Validation
- Expected Credit Loss (ECL) Computation
- Compare Model Discrimination and Accuracy 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