precision (ML)

Definitions

1. The fraction of positive predictions that are actually correct: true positives divided by (true positives + false positives). High precision means the model rarely cries wolf — when it says something is positive, it is usually right. Precision is critical in applications where false alarms are costly, such as spam filtering or fraud detection.

   In plain English: Of all the things the model said were positive, how many actually were. High precision means very few false alarms.

   Example: The spam filter had 99.2% precision — almost every email it flagged as spam truly was spam — but recall was only 78%, so some spam still got through.

Related Terms

• confusion matrix
• recall (ML)
• F1 score
• K-Nearest Neighbors