Programming the statistical procedures from SAS

Multinomial Logistic--no observations predicted in 3 of 6 dependent groups

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Multinomial Logistic--no observations predicted in 3 of 6 dependent groups

Hello. I am trying to build a multinomial logistic model using LINK=GLOGIT to predict customer membership in 6 groups. The customers are grouped into 6 groups based on survey responses to spend amount in the industry overall & with one particular vendor in the industry.
Behavioral data (retail purchase data) of these surveyed customers is what I'm using to try & develop a model to predict membership in one of the 6 groups. All solutions I've found result in nearly all modeled customers being placed into the largest segment (based on max score) & no customers being placed in some of the smaller segments. Basically, 93+% of those in each segment are scored to be placed into the largest segment, not the segment they actually fall in. The largest segment is 43% of all customers.

Any tips on how to alleviate this situation and reduce the very large rate of misclassifications? Different Proc options, data preparation & variable steps?

Thank you.

Message was edited by: c-cubed

Message was edited by: c-cubed
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Re: Multinomial Logistic--no observations predicted in 3 of 6 dependent groups

I don't have time for a full response right now, but look into the SCORE statement, in particular, look into PRIOR option within that.

HTH

Peter
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Re: Multinomial Logistic--no observations predicted in 3 of 6 dependent groups

Thank you Peter, but I have tried that & it didn't change the results.
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Re: Multinomial Logistic--no observations predicted in 3 of 6 dependent groups

There is always a chance that it is a non-predictive model. Have you looked at your model fit and discrimination statistics?
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