Precision and recall are two fundamental metrics used to evaluate the performance of classification models in artificial intelligence and machine learning. They help us understand how well a model is identifying relevant items (like spam emails, positive medical diagnoses, or objects in images) and avoiding mistakes. Precision is about how many of the items the model said were positive are actually correct, while recall is about how many of the actual positive items the model successfully found.
To break it down: imagine you built an AI system to detect spam emails. Precision answers the question, ‘Of all the emails my model labeled as spam, how many were truly spam?’ If your model flagged 100 emails as spam and 90 of them really were spam, your precision is 90%. High precision means your model makes very few false positives (wrongly labeling non-spam as spam).
Recall, on the other hand, answers, ‘Of all the actual spam emails in my inbox, how many did my model catch?’ If there were 120 spam emails in total and your model identified 90 of them, your recall is 75%. High recall means your model finds most of the true positives, but it might also make more mistakes by labeling non-spam as spam (more false positives).
Precision and recall are often in tension with each other. Increasing one can decrease the other, depending on how a model is set up. For example, if your model is very strict about labeling spam (only flagging the most obvious ones), it will have high precision but likely lower recall because it misses many actual spam emails. If it is more generous in flagging emails, it might catch more spam (higher recall) but also misclassify more legitimate emails (lower precision).
This balance is critical in real-world AI applications. In medical diagnoses, for instance, high recall ensures sick patients are not missed, but high precision reduces unnecessary stress or treatment for healthy patients. Depending on the context, sometimes precision is more important, sometimes recall, and sometimes a balance of both is needed. That’s why combined metrics like the F1 score exist, which is the harmonic mean of precision and recall.
Calculating precision and recall relies on four key numbers: true positives (the model correctly labels a positive case), false positives (the model incorrectly labels a negative case as positive), false negatives (the model misses positive cases), and true negatives (the model correctly labels negatives). Precision is true positives divided by the sum of true positives and false positives. Recall is true positives divided by the sum of true positives and false negatives.
Understanding precision and recall helps practitioners select the right threshold for their models and evaluate trade-offs. It is especially important in cases with imbalanced datasets, where one class (like fraud or disease) is much rarer than the other. In such cases, accuracy alone can be misleading, and precision and recall give a much clearer picture of model performance.
