For information about the workflow for developing credit scorecards, see Credit Scorecard Modeling Workflow.
|Create creditscorecard object to build credit scorecard model|
|Perform automatic binning of given predictors|
|Return predictor’s bin information|
|Summary of credit scorecard predictor properties|
|Modify predictor’s bins|
|Set properties of credit scorecard predictors|
|Binned predictor variables|
|Plot histogram counts for predictor variables|
|Fit logistic regression model to Weight of Evidence (WOE) data|
|Set model predictors and coefficients|
|Return points per predictor per bin|
|Format scorecard points and scaling|
|Compute credit scores for given data|
|Likelihood of default for given data set|
|Validate quality of credit scorecard model|
This example shows how to create a
bin data, display, and plot binned data information.
Credit Rating by Bagging Decision Trees (Statistics and Machine Learning Toolbox)
This example shows how to build an automated credit rating tool.
Use the credit scorecard workflow to create, model, and analyze credit scorecards.
The goal of credit scoring is ranking borrowers by their credit worthiness.
Use observation weights with the credit scorecard workflow to create, model, and analyze credit scorecards.
Troubleshooting results when using a