Accuracy
Accuracy is the share of predictions a model got right out of all predictions. It's the most intuitive metric and the most misleading — on imbalanced data, a model can score high accuracy while being useless, which is why it's rarely enough on its own.
Accuracy is the simplest score: correct predictions divided by total predictions. For a balanced, well-defined classification task it’s a reasonable headline number, and it’s what most people reach for first.
The trap is class imbalance. If 99% of cases are negative, a model that blindly says “negative” every time scores 99% accuracy and catches zero of the positives. That’s why accuracy alone is dangerous — it hides where the model actually fails. Pair it with precision and recall (or F1) to see the failures it conceals, and for AI agents, remember that a correct final answer says nothing about whether the agent took a sensible path to get there.
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