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)