What is the formula for calculating Precision?

Precision is defined as the ratio of true positive predictions to the total number of positive predictions made, which includes both true positives and false positives. The formula used to calculate Precision is:

[ \text{Precision} = \frac{\text{TP}}{\text{TP} + \text{FP}} ]

Where:

• TP (True Positives) represents the number of correctly predicted positive instances.

• FP (False Positives) indicates the number of instances that were incorrectly predicted as positive when they were actually negative.

This metric is essential in evaluating the performance of classification models, especially in scenarios where the cost of false positives is significant. A higher Precision means that when the model predicts a positive instance, it is more likely to be correct.

Other options suggest different measures:

• The second option, involving TP and FN, actually calculates Recall instead of Precision.

• The third option reflects a combination of Precision and Recall to compute F1 Score, which is a different metric focusing on the balance between Precision and Recall.

• The last option refers to a generic calculation that does not definitively represent the Precision metric, as it lacks consideration of false positives.

Thus, the correct choice clearly aligns with the established formula for Precision in classification tasks.