ChiMerge

https://sci2s.ugr.es/keel/pdf/algorithm/congreso/1992-Kerber-ChimErge-AAAI92.pdf

ChiMerge Algorithm uses Chi-squared statistic to discretize attributes (numeric). In toad, we firstly transform Char/Object attributes to numeric with WOE function. The Algorithm is clear in paper (i.e. ChiMerge Algorithm Part).

Stepwise Regression

https://link.springer.com/article/10.1007%2FBF02576123 [1]

https://www.sciencedirect.com/science/article/pii/S0950584917305153?via%3Dihub [2]

http://www.jstor.org/stable/1434071[3]

Stepwise Regression （Forward/Backward/Stepwise, i.e. [2] 3.6. Stepwise Linear Regression）is uesed to reduce Low Information Gain Attributes and simplify the Final Model.

The Stepwise Regression Process[2]:

_images/stepwise.png

Scorecard Transformation

John Wiley & Sons, Inc., Credit Risk Scorecards Developing and Implementing Intelligent Credit Scoring (Final Scorecard Production Part)

Formula:

Score = Offset + Factor ∗ ln (odds) #odds: good:bad

Score + pdo = Offset + Factor ∗ ln (2 ∗ odds) # pdo: points to double the odds

==>

pdo = Factor ∗ ln (2),

Factor = pdo / ln (2);

Offset = Score - Factor ∗ ln (odds)

For example, if a scorecard were being scaled where the user wanted

odds of 50:1 at 600 points and wanted the odds to double every 20

points (i.e., pdo = 20), the factor and offset would be:

Factor = 20 / ln (2) = 28.8539

Offset = 600 – 28.8539 * ln (50) = 487.123

==>

Each score corresponding to each set of odds:

Score = 487.123 + 28.8539 * ln (odds)

Scorecard is developed with WOE as input, the formula can be modified as:

_images/scorecard.png

WOE = weight of evidence for each grouped attribute

β = regression coefficient for each characteristic

a = intercept term from logistic regression

n = number of characteristics

k = number of groups (of attributes) in each characteristic