random forest with specified false positive and sensitivity
You can do a grid serarch over the 'regularazation' parameters to best match your target behavior.
Parameters of interest:

max depth
number of features
Using the randomForest package in R, I was able to train a random forest that minimized overall error rate. However, what I want to do is train two random forests, one that first minimizes false positive rate (~ 0) and then overall error rate, and one that first maximizes sensitivity (~1), and then overall error. Another construction of the problem would be: given a false positive rate and sensitivity rate, train two different random forests that satisfy one of the rates respectively, and then minimize overall error rate. Does anyone know if theres an r package or python package, or any other software out there that does this and or how to do this? Thanks for the help.