What is Precision and Recall?

What Are Precision and Recall?

Precision and Recall are two fundamental metrics used to evaluate how well a classification model performs, particularly when the costs of different types of errors are not equal. Together, they provide a much more nuanced picture of model performance than simple accuracy, which can be deeply misleading for many business applications.

Precision answers the question: "Of all the items the model flagged as positive, how many were actually positive?" A model with high precision makes very few false alarms.

Recall (also called sensitivity) answers the question: "Of all the items that were actually positive, how many did the model successfully detect?" A model with high recall misses very few true positives.

Why Accuracy Alone Is Not Enough

Consider a fraud detection system processing 10,000 transactions, of which 50 are actually fraudulent:

• A model that simply labels everything as "legitimate" achieves 99.5% accuracy -- but catches zero fraud
• A model with 80% precision and 90% recall catches 45 of the 50 fraudulent transactions (recall = 90%) and for every 10 transactions it flags, 8 are actually fraud (precision = 80%)

The second model has lower overall accuracy but is vastly more useful for the business. This is why precision and recall exist -- they measure what matters for problems where the interesting cases are rare.

The Precision-Recall Tradeoff

In nearly every classification system, there is an inherent tradeoff between precision and recall:

• Increasing precision (fewer false alarms) typically decreases recall (more missed positives). The model becomes more conservative, only flagging items it is very confident about.
• Increasing recall (catching more true positives) typically decreases precision (more false alarms). The model becomes more aggressive, casting a wider net.

This tradeoff is controlled by the classification threshold -- the confidence level at which the model decides to flag an item as positive. A higher threshold increases precision but reduces recall; a lower threshold does the opposite.

The F1 Score

The F1 score is the harmonic mean of precision and recall, providing a single number that balances both metrics:

• F1 = 2 x (Precision x Recall) / (Precision + Recall)
• An F1 score of 1.0 means perfect precision and recall
• An F1 score of 0.0 means the model has completely failed on at least one metric

The F1 score is useful for quick comparisons but should not replace examining precision and recall individually, as businesses typically care more about one than the other.

Choosing Between Precision and Recall

The right balance depends entirely on the business cost of each type of error:

Prioritize Precision When:

• False alarms are expensive or disruptive
• Human reviewers must investigate every flagged item, and their time is costly
• False accusations damage customer relationships or brand reputation
• Example: Email spam filtering -- flagging a legitimate email as spam (false positive) is very disruptive, so precision matters more

Prioritize Recall When:

• Missing a true positive is expensive or dangerous
• The cost of investigation is low compared to the cost of a missed case
• Safety or compliance is at stake
• Example: Cancer screening -- missing an actual cancer case (false negative) is far worse than ordering additional tests (false positive), so recall matters more

Real-World Business Applications

Precision and recall guide critical decisions across industries in Southeast Asia:

• Fraud detection -- Banks in Singapore and across ASEAN must balance catching fraudulent transactions (recall) against blocking legitimate transactions (precision). Blocking too many legitimate transactions frustrates customers; missing too much fraud creates financial losses.
• Manufacturing quality control -- Factories must balance detecting all defective products (recall) against minimizing false rejects of good products (precision). Missing defects risks customer complaints; over-rejecting wastes good inventory.
• Customer churn prediction -- Retention teams have limited capacity. High precision ensures they spend time on customers who are actually at risk; high recall ensures they do not miss customers who will churn.
• Document classification -- Automated systems routing customer inquiries to departments need balanced precision and recall to avoid both misdirected tickets and unrouted tickets.
• Content moderation -- Platforms must balance removing harmful content (recall) against preserving legitimate content (precision), a challenge that is particularly nuanced across diverse Southeast Asian cultural contexts.

Practical Considerations

Class Imbalance

Precision and recall are especially important for imbalanced datasets where one class is much rarer than the other. In these scenarios, accuracy is meaningless (predicting the majority class always gives high accuracy), but precision and recall reveal the true performance on the minority class you care about.

Multiple Classes

For problems with more than two categories, precision and recall are computed per class and can be averaged in different ways:

• Macro average -- Simple average across all classes, treating each class equally
• Weighted average -- Average weighted by the number of examples in each class
• Micro average -- Computed globally across all predictions

Threshold Tuning

Rather than accepting the model's default threshold, businesses should tune the classification threshold to achieve the precision-recall balance that aligns with their specific cost structure. This is a simple but high-impact optimization that many teams overlook.

The Bottom Line

Precision and recall are indispensable metrics for any business deploying classification models. They reveal the types of errors a model makes and enable you to tune the system to align with your specific business costs. The key insight for business leaders is that overall accuracy can be deeply misleading -- what matters is whether your model makes the right tradeoffs between false alarms and missed detections for your particular use case.