Precision Score is a metric used to evaluate the Accuracy of a Classification model, specifically focusing on the positive class.
How many retrieved items are relevant?
This metric indicates the accuracy of positive predictions. The formula for precision is:
where:
- TP (True Positives): The number of correctly predicted positive instances.
- FP (False Positives): The number of instances incorrectly predicted as positive.
Importance:
- Precision is crucial in scenarios where the cost of false positives is high, such as in spam detection or medical diagnosis. It helps in understanding how many of the predicted positive instances are actually positive.
