Precision
Precision is a classification metric that measures the proportion of positive predictions made by a model that are actually positive. It indicates how often a model is correct when it predicts the positive class.
What is Precision?
Precision is calculated as the number of true positives divided by the total number of positive predictions, including both true positives and false positives. A high precision means the model produces relatively few false positive predictions.
Why is Precision Important?
Precision is especially important when false positives are costly or undesirable. It helps teams understand how reliable a model's positive predictions are and is often considered alongside recall to evaluate classification performance.
Common use cases
Precision is commonly used to evaluate spam detection, fraud detection, medical diagnosis, content moderation, information retrieval, and other classification systems.