You own a binary classifier that filters spam from legitimate email in a large productivity platform. The current model is a logistic regression system that assigns a spam probability, and messages above a 0.50 threshold are sent to the spam folder while the rest stay in the inbox. Customer support says users are complaining about both junk reaching the inbox and important messages being hidden, and a product manager asks you which evaluation metrics should matter most and how to interpret the model's current performance.
| Metric | Validation Set |
|---|---|
| Accuracy | 0.952 |
| Precision | 0.741 |
| Recall | 0.588 |
| F1 Score | 0.656 |
| AUC-ROC | 0.903 |
| True Positives | 2,940 |
| False Positives | 1,028 |
| False Negatives | 2,060 |
| True Negatives | 43,972 |
How would you explain the common classification metrics in this setting, interpret what these numbers say about model quality, and recommend which metrics the team should prioritize for monitoring and improvement?