EDUCATION & TRAINING
From Raw Data to Risk Classes
Towards Data Science
About This Tutorial
Categorization is a crucial step in credit scoring that transforms raw variables into stable risk classes, making the relationship between variables and default risk clearer, stable, and easier to use in a model. This process is particularly useful for logistic regression models, which are widely used in credit scoring due to their transparency and interpretability. By categorizing raw variables, credit risk models can reduce the impact of outliers, handle missing values, and capture non-linear risk patterns. This step is essential for creating reliable and stable credit scoring models.