I am trying to analyze lending club data and want to predict whether a loan is risky or safe using random forest with decision tree as a classifier. The data is imbalanced. It contains one-fourth of Risky Loans and three-fourths of Safe Loan. I did a stratified split of the data into Training and Testing set. Then, I applied SMOTE to the training set and ran 10 fold CV to chose my model. I was getting good sensitivity around 70%. But after I have selected my model and run it on the test set (with 3:1 ratio of class) I got a sensitivity of 15%. How do I improve sensitivity?