I have 100,000 observations (9 dummy indicator variables) with 1000 positives. Logistic Regression should work fine in this case but the cutoff probability puzzles me.
In common literature, we choose 50% cutoff to predict 1s and 0s. I cannot do this as my model gives a maximum value of ~1%. So a threshold can be at 0.007 or somewhere around it.
I do understand
ROC curves and how the area under curve can help me choose between two LR models for the same dataset. However, ROC doesn't help me choose an optimum cutoff probability that can be used to test the model on an out-of-sample data.
Should I simply use a cutoff value that minimizes the
misclassification rate? (http://www2.sas.com/proceedings/sugi31/210-31.pdf)
Added --> For such a low event rate, my misclassificiation rates are affected by a huge number of false positives. While the rate over all appears good as total universe size is also big, but my model should not have so many false positives (as it is an investment return model). 5/10 coeff are significant.