Precision is a metric used in binary classification problems to answer the following question: What proportion of positive predictions was actually correct?

Precision = 1 means the model’s predictions are perfect, all samples classified as the positive class are truly positive.

If a medical test that has high precision shows that a patient has a disease, there is a high likelihood that the patient does, in fact, have the disease. However, the test could still fail to identify the presence of the disease in many patients! This is because precision is only concerned with positive predictions.


Precision is defined as:

\[\text{Precision} = \frac{\text{True positive}}{\text{True positive} + \text{False positive}}\]

True positive is when actual positive is predicted positive, and
False positive is when actual negative is predicted positive.

Read more about this in the Confusion matrix entry in the glossary.

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