# AnalyticBridge

A Data Science Central Community

We use linear scaling to calculate score/points for an applicant. Now, if we were to determine the number of points for each attribute of a characteristic, Naeem Siddqui, gives the following equation (Image attached).

I understand that it is nothing but dividing the total # of points into each attribute of each characteristic which is why we are dividing the offset and the intercept term by number of characteristics, however, what perplexes me is the negative term.
ln(odds) has been replaced by the logit equation with a negative term outside the bracket. I am unable to understand from where did the negative sign appear.

Thanks

Tags: Credit, Risk, Scorecards

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### Replies to This Discussion

I presume that the negative sign is simply due to the fact that most definitions of WOE define it as ln(%Goods/%Bads) while the score is proportional to ln(odds), where odds is usually defined as Probability(Bad) / Probability(Good) in risk models... Therefore score ~ -WOE.

I would suggest checking the context where you found this formula to check if this is actually the case.

ranking or predicted probability of a good compared with a bad explains whether the author definging good=1 or otherwise basically direction of the scorecard. also
Once you have calculated the score and points, sum of the points should add to the score. Now examine this with the previous mentioned score direction validates the reasoning of the sign '-'