I am trying to understand Logistic Regression in relation to credit scoring model. I wish to understand the significance of "20/ln(2)" in logistic regression. Why and how is it used?
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This is a common scaling factor used for credit scoring models built with logistic regression. The interpretation of the dependent variable in logistic regression is as log odds, but in credit scoring, we like to deal in points, thus a scaling factor is applied to the log odds to convert to the point system. A widely used convention in credit scoring is the concept of "Points to Double the Odds" (often abbreviated PDO), and this is the source of the $\ln (2)$ in the question. For example, how many points does the score change if the odds increase from 100:1 to 200:1. A common default value for PDO is 20, because it produces credit score ranges that people tend to like. So, the interpretation of the $20/\ln(2)$ is that for a 20-point increase in score, the odds double. |
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