There are several metrics used to evaluate the performance of an email marketing model:
1. Accuracy Accuracy measures the proportion of correct predictions made by the model. While it's a useful metric, it may not be sufficient for imbalanced datasets where one class is significantly more frequent than the other.
2. Precision and Recall Precision measures the proportion of true positive predictions among all positive predictions, while recall measures the proportion of true positive predictions among all actual positives. These metrics are particularly useful in evaluating models where the cost of false positives and false negatives is high.
3. F1 Score The F1 score is the harmonic mean of precision and recall. It provides a single metric that balances both precision and recall, making it useful for evaluating models where both false positives and false negatives are critical.
4. ROC-AUC The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) are used to evaluate the trade-off between true positive and false positive rates. AUC provides a single metric that summarizes the model's performance across different threshold values.