I`ve got a classification problem in which one class has only 1%(Failures) instances. tried various classification algorithms with upsampling and downsampling(1:1,1:2,1:4,1:6) unfortunetly ,nothing has worked out(model overfits on train). Problem is that my model is able to capture Failure pattern but a small chunk of miss-classified 0(majority class) makes value of precision to be very very low.
currently using Random forest but precision is coming very low and it over-fits on train set,
over net people talk about Penalizedsvm/LDA or using cost function in SVM. but, i dont know how to implement them in R.
could anybody help , if has faced same scenario.