I just ran a classification report after training my model, and I see precision, recall, and f1-score. I get the basic idea, but how do I use these metrics to decide if my model is actually ready for deployment? When should I prioritize precision over recall?
Model deployment readiness is determined by aligning precision and recall metrics with the specific cost-benefit trade-offs of the business application, prioritizing precision to minimize false positives and recall to minimize false negatives.
1 answer
A model is ready for deployment only when its performance metrics satisfy your specific business requirements, not when the numbers simply look high. Precision is prioritized when the cost of a false positive is high, such as in fraud detection, whereas recall is vital when missing a true case is dangerous, like in cancer diagnostics.
