How to convert coefficients of logistic regression into score values when constructing scorecard?

Question

I have built a logistic regression model which takes a dataframe of dummy values as an input and produces binary classification (0 for accept, 1 for default). I have already split all variables into ranges and calculated logistic regression coefficients for each of these ranges.

Here is the sample of what my input looks like:

(pictures are taken from this article)

And coefficients for these inputs (which are taken from the coefficients of logistic regression model):

I want to convert these values into scores, but I am not sure how to accomplish this. I saw some texts and questions here that mention PDO and linear scaling. But I cant figure out how to actually produce score values.

Any help would be appreciated

Answer 1

You need to take some course in logistic regression and not only follow tutorials as the one you linked. Several points:

• You seem to bin continuous variables before using them as predictors. That is mostly a bad idea, a better idea is to model using regression splines. See What is the benefit of breaking up a continuous predictor variable? and Violation of linearity assumption in Logistic Regression (and search this site).

• You say I want to convert these values into scores, but I am not sure how to accomplish this, which is strange. The predicted value for each case in LR is a probability value, and that is your score. See Why P>0.5 cutoff is not "optimal" for logistic regression? and Why isn't Logistic Regression called Logistic Classification?