Can Evaluation Metrics Help in Understanding Model Performance?
Yes, using appropriate evaluation metrics is crucial in understanding how well a model performs on imbalanced datasets. Common metrics include:
- Confusion Matrix: Provides a detailed breakdown of true positives, true negatives, false positives, and false negatives. - Precision-Recall Curve: Useful for understanding the trade-off between precision (positive predictive value) and recall (sensitivity). - F1 Score: The harmonic mean of precision and recall, providing a single metric that balances both concerns. - Area Under the ROC Curve (AUC-ROC): Measures the ability of the model to discriminate between the classes.
