Following your suggestions, I have done the following process: 1) Fit the model 2) take the scoring variable of the target 3) Make the low P_1 target response as 0 e.g. if P_1<.50 then response=0 /* Incidence rate is very low and most of the predictors value are missing due to customer inactivity for a long time */ 4) run the logistic regression model again by using P_1 as a predictor 5) fitting the model 6) Scoring the new dataset based on that model. Please find below the code - I'm using: /********************************************************************/ /* fit logistic regression model */ /********************************************************************/ proc logistic data = RPT_Response_XX_2_2 outmodel = rpt_response_XX_model_param descending namelen = 32 ; model response = &munvar P_new / selection = stepwise link = logit outroc = trn_outroc roceps = 0.0001 ctable pprob = (0.00 to 1.00 by 0.01) ; output out = XX_churn_mod_pred predprobs = individual ; run; quit; /********************************************************************/ /********************************************************************/ /********************************************************************/ /* score dataset */ /********************************************************************/ /********************************************************************/ /********************************************************************/ proc logistic inmodel = rpt_response_XX_model_param; score data = rpt_response_XX_all out = RPT_Response_XX_scored_data outroc = trn_outroc ; run; After having the score data sets , how could I interpret the P_1 ,Is that also weigh up probability scores for the same type of customers as like as missing response(real target) . I'm really stuck on that point. Your quick guidance is highly appreciated. Thanks again for your suggestions.
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