I have just completed the material for "Module 1: Predictive Modeling" course and I would appreciate if someone could clarify/confirm how the logic for decisions work:
Thanks
Hi:
Here's feedback from the course instructors:
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Hope this helps clarify the default and cut-off for you.
Cynthia
Hi:
Here's feedback from the course instructors:
---------------------------------
------------------------------------
Hope this helps clarify the default and cut-off for you.
Cynthia
Thank you for your answer!
Just a quick point: the default 0.5 cut-off, does it apply regardless of the proportions used in the sample? For instance, assuming I do oversampling with a ratio of 0.3 primary outcome / 0.7 secondary outcome, and I do not specify any prior probabilities, am I right in saying 0.5 would still be used as the default cut-off for classification purposes (based on the posterior probabilities)?
Hi:
The instructors' feedback on this question is:
"The default cut-off is always 0.5, regardless of whether or not any sampling has been done to deal with a rare event level. If the data has been over-sampled to deal with a rare event, then use decision processing (i.e., define prior probabilities) which will adjust the cut-off accordingly. If over-sampling is done and prior probabilities are not defined, than 0.5 will be used as a cut-off."
Cynthia
1. That may be the default but you can change that with the CUTOFF option in PROC LOGISTIC. Not sure about decision trees because it follows a different algorithm.
2. Yes, see the CUTOFF option for PROC LOGISTIC.
The 0.5 cutoff is usually the default but you can change it as needed.
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