Definition
Accuracy is the proportion of evaluated predictions counted as correct. For binary classification, it is commonly calculated as (true positives + true negatives) / all evaluated examples. That compact formula hides several choices: what counts as an example, which label is positive, how abstentions and partial answers are scored, and whether each example receives equal weight.
Accuracy is useful when the classes are reasonably balanced and mistakes have similar costs. It can be badly misleading when one class dominates. A detector that labels every transaction legitimate could be 99 percent accurate in a dataset with 1 percent fraud while catching no fraud at all.
Report the numerator and denominator, not only the percentage. For stochastic systems, also report the number of trials and uncertainty across runs. For multi-step agents, state whether accuracy applies to final outcomes, individual steps, tool calls, or rubric items.
Distinguish it from nearby terms
- Precision asks how often positive predictions are correct. Recall asks how many actual positive cases were found.
- Exact-match accuracy counts only complete string or answer matches. A rubric may award partial credit instead.
- Calibration asks whether stated probabilities match observed frequencies. A system can be accurate but overconfident, or calibrated but not very accurate.
Check your understanding
A classifier is 99 percent accurate on a dataset where 99 percent of examples are negative. What additional counts or metrics would you request before trusting it?