For information about the workflow for developing credit scorecards, see Credit Scorecard Modeling Workflow.
|Perform automatic binning of given predictors|
|Return predictor’s bin information|
|Summary of credit scorecard predictor properties|
|Replace missing values for credit scorecard predictors|
|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|
|Fit logistic regression model to Weight of Evidence (WOE) data subject to constraints on model coefficients|
|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|
|Create compact credit scorecard|
Feature Screening with screenpredictors (Risk Management Toolbox)
This example shows how to perform predictor screening using
screenpredictors (Risk Management Toolbox).
This example shows how to create a
bin data, display, and plot binned data information.
To compute scores for a
creditscorecard object with constraints for equality, inequality, or bounds on the coefficients of the logistic regression model, use
This example shows alternative workflows to handle missing values when working
Comparison of Credit Scoring Using Logistic Regression and Decision Trees (Risk Management Toolbox)
This example shows the workflow for creating and comparing two credit scoring models: a credit scoring model based on logistic regression and a credit scoring model based on decision trees.
Use Reject Inference Techniques with Credit Scorecards (Risk Management Toolbox)
This example demonstrates the hard-cutoff and fuzzy augmentation approaches to reject inference.
This example shows how to build an automated credit rating tool.
compactCreditScorecard Object Workflow (Risk Management Toolbox)
This example shows a workflow for creating a
compactCreditScorecard object from a
This example shows how to perform imputation of missing data in the credit scorecard workflow using the k-nearest neighbors (kNN) algorithm.
This example shows how to perform imputation of missing data in the credit scorecard workflow using the random forest algorithm.
This example shows a workflow to gather missing data, manually treat the training data, develop a new
creditscorecard, and treat new data before scoring using the MATLAB®
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