Interpret F1 for Spam Detection

Context

MailShield uses a binary classification model to detect spam emails for small business inboxes. The team reports strong overall accuracy, but users still complain that too many spam messages reach the inbox while some legitimate emails are incorrectly filtered.

Current Performance

Confusion Matrix

The Problem

The product manager wants to know whether F1 is the right headline metric for this model and what the current F1 score actually says about model quality. Because spam is relatively rare, the team suspects accuracy is overstating performance.

Requirements

1. Define the F1 score and explain how it is calculated from precision and recall.
2. Interpret the current F1 score of 0.709 in the context of this spam detection problem.
3. Explain when F1 should be preferred over accuracy.
4. Discuss the tradeoff between precision and recall for this use case.
5. Recommend how MailShield should improve the model or threshold depending on whether the business prioritizes fewer missed spam emails or fewer false spam flags.

Constraints

• False positives hide legitimate customer emails and create trust issues.
• False negatives allow spam into inboxes and reduce product quality.
• The team can adjust the classification threshold quickly, but full retraining takes 10 days.