# Optimizing False Negative Rate after Logistic Regression

I created a probit model and tested it against a random sub sample of my dataset. I am interested specifically in seeing how many data points I can predict to be FALSE without having too many that are actually TRUE. Using the threshold of 0.1 (see below), I was able to predict to be FALSE about 30% with a false negative rate of 2.5%.
However, I don't know if this is optimal.

Is there a way for me to pick my threshold that maximizes my FALSE predictions while minimizing my false negatives?

glm.fit=glm(Outcome~A+B+C+D+E+F+G,data=myData,family=binomial(link="probit"))
test=mysample <- myData[sample(1:nrow(myData),10000,replace=FALSE),]
glm.probs =predict(glm.fit,test, type="response")
glm.pred=rep(0,10000)
glm.pred[glm.probs>.1]=1
x <- sum(glm.pred == 0 & test$Outcome == 1) y <- sum(glm.pred == 0)  • I'm guess you mean a$threshold\$ that maximizes your True negative rate while minimizing your false negative rate? – Andrew Cassidy Mar 7 '14 at 17:30
• I believe this has been covered here before. You're choosing a point on the AUC curve. To do so you need a cost/utility function that balances False Negatives/False Positives. Without the cost function you can't optimize anything. – charles Mar 7 '14 at 17:30
• If I predict it will be TRUE, then I have to do x amount of work. If I predict it will be FALSE, then I don't have to do any work. So I want to do as little work as possible but if I don't do the work, there is a larger cost associated with that, and obviously a smaller cost associated with doing that work up front. – zu2122 Mar 7 '14 at 17:32
• @charles you mean ROC curve not AUC curve – Andrew Cassidy Mar 7 '14 at 17:33
• @AndrewCassidy yes! – charles Mar 7 '14 at 17:46