Metric Tradeoff Deck
Recall how common AI evaluation metrics behave under different error-cost tradeoffs.
comparison battlecard Recall vs precision Recall asks how many real positives the model catches. Precision asks how often model-positive cases are truly positive. Choose recall when missed positives are costly; choose precision when false alarms are costly. Can we just use accuracy? Only if classes and error costs are roughly balanced. With rare high-risk cases, accuracy can look high while the model misses the mission. Ask for class balance and false-negative cost before accepting accuracy. When is F1 useful? When precision and recall both matter and their costs are close enough that a harmonic mean is meaningful. Latency is not…
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