Precision and Recall are two fundamental metrics used to evaluate the performance of a Classification model, particularly in binary classification tasks.
They are related through a trade-off: improving one often comes at the expense of the other.
Key Differences
Precision focuses on the quality of positive predictions, defined as the proportion of true positive predictions among all positive predictions made by the model.
Precision=TP+FPTP
Recall focuses on the ability to identify all relevant positive instances, defined as the proportion of true positive predictions among all actual positive instances.
Recall=TP+FNTP
Trade-off
It is challenging to optimize both precision and recall simultaneously. Improving precision by reducing false positives may lead to an increase in false negatives, thereby reducing recall, and vice versa.
The choice between prioritizing precision or recall is task-dependent.
Task Dependency Example
In a spam classification task, it might be more important to avoid moving important emails to the spam folder (high precision) than to occasionally allow spam emails into the inbox (lower recall). Thus, precision is prioritized over recall in this context.
