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I am working on building a Credit Scorecard model. Till now I have performed the following steps:

  1. Data preparation and cleansing
  2. Calculate WOE and IV values
  3. Model Fitting

Now I working on calculating scores for each variable. For this, I need to know 'Target Score', 'Target Odds', and 'pdo'. How these 3 variables are calculated? I have gone through some article but everywhere they assume some Target Score and other variables. One of the article link:

Before this standard approach, I did the following:

  1. Data cleansing, transformation(select transformation with best predictive power), and stepwise logistic regression.

Use the variables selected by the stepwise model and build the final model on it.

I have a couple of question from this:

  1. Which is the best way out of the above 2 approaches?
  2. What if the variables selected based on IV and stepwise are different?
  3. Is it right to compare both the approaches?

Thanks

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1 Answer 1

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Target score, target odd, and PDO are not calculated. They are set by the analyst. These 3 values are used to "scale" the raw scores produced by a statistical model. Why is scaling important? Scaling provides some sort of standardization, which is especially useful when you have different scoring models across different portfolios.

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