Difference Between Precision and Accuracy

Published 2026-02-10 in Deep Learning

Precision and accuracy are two commonly used evaluation metrics in machine learning, but they measure different aspects of model performance . Although they are often confused, understanding the difference between them is crucial—especially in real-world applications such as medical diagnosis, fraud detection, and spam filtering. The image uses a target board analogy to visually explain how precision and accuracy behave under different scenarios. What Is Accuracy? Accuracy measures how close predictions are to the true or correct value . In classification problems, accuracy tells us the percentage of total predictions that are correct. High accuracy means most predictions are correct overall. What Is Precision? Precision measures how consistent or repeatable the predictions are. In classification problems, precision focuses on how many of the predicted positive results are actually positive. High precision means the model makes very few false positive errors. Visual Interpretation of the Image 1. High Accuracy, Low Precision The shots are spread out but centered around the target. Predictions are close to the true value on average Individual predictions are inconsistent Correct…

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