Reducing false positive rate I am running a model for a problem in insurance domain. The final results show some false positive x and some false negative y. I am using SAS Enterprise Miner for this. Can somebody suggest me how to reduce false positive? I know for this i have to increase the false negative. I want to know two things:  


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*Is there any option in e-miner where I can give more weight to false negative and less to false positive?

*Is there any general approach in modeling which tells us any ways to reduce false negatives or is it just a hit and trial approach?
 A: What you can do if you do not find the weight option is create the same effect yourself, by increasing the amount of the positives, for example you can give as an input to the algorithm 2 times each of the known positives an leave the negatives as they where. You can even increase it 10 times, it is a matter of experimenting to get as near as you can to the best possible result.
A: Regarding first (and second) question: A general approach to reduce misclassifications error by iteratively training models and reweighting rows (based on classification error) is Boosting. I think you might find that technique interesting.
Regarding second question: The question sounds a little bit naive to me (but I maybe did not understand your true intention), since reducing misclassification error = improving model performance is one of the challenges in Data Mining / Machine Learning. So if there were a general all-time working strategy, we all would have been replaced by machines (earlier than we will anyways). So I think that yes, the general approach here is educated trial and error. I suggest this question, Better Classification of default in logistic regression, which may give you some ideas for both questioning and model improvement.
I suggest to play around a little bit and then come back to ask more specific questions. General questions regarding model improvement are hard to answer without data and/or additional information of the circumstances. Good luck !
A: Under Model Selection, choose "Validation Misclassification" as your model selection criterion. This will select the model with the lowest misclassification rate. Or use the profit/loss matrix and attach a cost function to your false positive or false negative.
A: Yes, it is called as the cutoff under the assess tab. You have to run the whole thing once to check the graphs to decide your optimal cut off (i.e., more true positive or more true negatives based on all rates). Place the cut off module after each model (regression, tree, etc), and check the results for that module. You can then change the user specified value of the cutoff point to get the exact rate for the TP/TN or the overall symmetric misclassification rate. Then run the whole thing again.
