Database Reference
In-Depth Information
5) Create your own scoring data set using the attributes in the training data set as a guide.
Enter at least 20 observations. You can enter data for people that you know (you may
have to estimate some of their attribute values, e.g. their credit score), or you can simply
test different values for each of the attributes. For example, you might choose to enter
four consecutive observations with the same values in all attributes except for the credit
score, where you might increment each observation's credit score by 100 from 400 up to
800.
6) Import your scoring data set and apply your model to it.
7) Run your model and review your predictions for each of your scoring observations.
Report your results, including any interesting or unexpected results.
Challege Step!
8) See if you can experiment with different lower bounds for each attribute to find the point
at which a person will be predicted in the 'DO NOT LEND' category. Use a combination
of Declare Missing Values and Replace Missing Values operators to try different thresholds
on various attributes. Report your results.
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