I am working with an imbalanced data: 70k:0 and 1K:1 with 12 features. I would like to perform classification to choose the important features. So far, I have done under-sampling, over-sampling, hybrid (over and then under-sampling), SMOTE, but performance metrics are terrible. I have tuned my model as well for decision trees and random forest. In case of SMOTE, here are my results. What should I do to improve the performance?
f1-score = 0.06 precision = 0.03 recall = 0.48 accuracy = 0.77 AUC = 0.70 Confusion matrix: [[13685 4082] [134 125]