This project will explore on using various evaluation metrics to optimize the performance of the chosen classifier to solve the binary Fraud Classification problem.
- Accuracy alone is sufficient to get a complete picture of the performance of classifier
- Various evaluation metrics such as tradeoffs between precision and recall, roc curve, and confusion matrix
- Learn how to search for the best combination of model parameters such as various values of regularisation strength and regularisation level
