Artificial Intelligence & Machine Learning
Precision
Definition
In classification tasks, precision is a metric that measures the proportion of positive predictions that were actually correct. It answers the question: "Of all the times the model predicted positive, how often was it right?"
Why It Matters
Precision is important when the cost of a false positive is high. You want to be very sure when the model predicts something is positive.
Contextual Example
In a spam filter, you want high precision. You would rather a few spam emails get into the inbox (false negatives) than have an important email be incorrectly marked as spam (a false positive).
Common Misunderstandings
- Precision = True Positives / (True Positives + False Positives).
- There is often a trade-off between precision and recall.