Use the Credit Scoring for EM product which provides variable selection, interactive or batch classing (binning) including weights of evidence calculations, scorecard construction with scaling and diagnostics, and reject inference.
Use the Credit Scoring for EM product which provides variable selection, interactive or batch classing (binning) including weights of evidence calculations, scorecard construction with scaling and diagnostics, and reject inference.
1. The exercise per se is not a modeling process. Just a transformation, if you would. Or a mapping exercise.
2. Score-carding generally is industry-specific. There is general-purpose scorecard and credit application specific score card. As Wayne indicated, for credit scoring business, it may be fairly straightforward to either use EM credit score application or run the log transformation as indicated in the spreadsheet (through Score node or just data steps). EM, however, has a much broader user base than credit risk modeling. Many users do not use log transformation to map probability scores to card allocation. If you google, you should find people focusing on all kinds of scaling exercises involving score card
3. If you really need to see a custom node (icon) in EM to conduct a focused, custom task for yourself (I know some users like to click button to get things done), consider extending EM node, although I personally believe EM SAS Code node should be easy kill for this exercise.
4. If you goal is for model performance comparison, you can use Model Import node in EM to get the probability score into EM and combine it into Model Comparison node in later steps.
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Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
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