I'm tasked with predicting customer churn, and given hundreds of variables with which to create a binary classification model.
In addition to producing a predicted probability of churn for each customer in a test set, I'd also like to add the top feature(s) driving a high probability of churn. For example, an ideal output table might look like this:

customer #  churn_prob  top_feature_1  top_feature_2 
----------  ----------  -------------  -------------
1001        0.95        featureC       featureH
1002        0.78        featureA       featureK
1003        0.43        featureB       featureC

That would tell me, for example, customer 1001's high churn risk is driven most by featureC, then featureH.

I seek to give my internal clients SOMETHING actionable for each external customer to help them reduce churn. How does one go about this?

  • $\begingroup$ Did you ever resolve this? I am interested in this as well. $\endgroup$ – B_Miner May 9 '16 at 13:43

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