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Posted 04-03-2019 10:35 AM
(673 views)

Hello,

I have a logistic regression model which predicts likelihood of a customer to churn. The predicted probability scores range between .4299 to .00094.

Somebody confused me by saying 'The score of less than .5 means, less probability of churn as compared to 'average'. I think its incorrect because:

The average churn rate of my sample is 2%. This means 2/100 = .02 probability score.

Hence the highest probability score of .4299 means much higher chance to churn than average. Not necessarily the highest probability has to be above .5 score.

Is my interpretation correct?

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Depends on what event you modeled, assuming you modeled the event of a customer churning (which you should likely be using PROC PHREG/survival analysis) then 0.4299 means that's the probability of churning. I'm surprised your range is less than 0.5 the entire distribution you should verify your model first.

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Sorry, I mean the highest score is .4299

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