# Low model performance for an imbalanced data, is there any hope to improve the metrics?

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]

• Please see this discussion about imbalanced data, this discussion about SMOTE and similar approaches, and this discussion about why you should consider different performance metrics.
– EdM
Mar 24 at 14:32
• Thant you! Could the low performance be because my X features are nonsense? Mar 24 at 15:28