Evaluation Metrics for Unbalanced data

The most popular reported metrics namely Accuracy, Precision, Recall, and F1-score are not good indicators when the data is imbalanced. Also, some of these matrices are not symmetric i.e. if we exchange just the labels of positives and negatives, good measures may turn poor and vice versa.In this work, I did a robust comparison of various evaluation metrics on data imbalance and symmetry and analyse their results. Furthermore, I proposed a new evaluation metrics, TPNR, which is robust to these changes.

GitHub link: https://github.com/asawaswapnil/Machine-Learning-Final-Project-With-Python