Case context: A model flags 80 emails as spam on a validation set. When the team checks those 80 predictions against the true labels, 68 really are spam and 12 are legitimate messages.

Question: Using the definition of Precision, what is the Precision of this model on the validation set, and what does that tell you about its spam predictions?

Sample answer: Precision is the share of predicted spam messages that are truly spam, so the Precision is 68/80 = 0.85, or 85%. This means that when the model marks an email as spam, it is right 85% of the time. It describes how dependable the spam alerts are, not how many spam emails the model found overall.

Key points:

• Precision = true positive predictions / all positive predictions
• 68/80 = 0.85 = 85%
• Higher Precision means predicted positives are more reliable
• Precision does not measure how many real spam emails were missed

Rubric: Full credit requires correctly computing Precision as 68/80 and correctly explaining that it measures the reliability of the model’s positive predictions, not its coverage of all actual spam emails.