12-05-2013 11:39 AM
I have built a logistic regression model to predict customers who are more likely to click on a marketing offer. My model is not great as I have a AUC=0.58 and 50% capture only 60%. So I have built the model on a training sample (9 months) then kept 2 months of data as sample held out from model dev (Validation) to test my prediction. Once I finished to build the model on the training, I have scored the validation (2 months dataset) then sorted the scored validation, and sliced the target variable and probabilities into deciles ..like below...It doesn't seem that my model is ranking well ...Is it because the AUC is low..so the predictive power is not great? Your help will be much appreciated....Many Thanks
proc rank data=VALIDATION (KEEP=CLICK_FLAG EM_EVENTPROBABILITY) groups=10 out=pred_valid;
var EM_EVENTPROBABILITY; ranks decile;
proc sort data=pred_valid;
by descending EM_EVENTPROBABILITY;
proc means data=pred_valid n mean sum;
proc means data=pred_validn mean sum;
|Decile||Training Response Rate||Training Mean_Score||Validation Response Rate||Validation Mean_Score|
12-06-2013 04:16 PM
Kanyange, would you mind sharing your modeling code? It would help to understand the options you selected, along with a snapshot of the output.
12-12-2013 09:23 AM
Kanyange, this question would be best posted in the SAS Stat Community
Given your needs with PROC LOGISTIC, they would be best to respond appropriately.
Let me know if you need any more help.