Precision in machine learning is a metric used to evaluate the accuracy of a classification model, defined as the ratio of true positive predictions to the total predicted positives (true positives plus false positives).
The Significance of Precision
Precision is particularly important in scenarios where the cost of false positives is high. For example, in spam detection, a high precision model would correctly label as many non-spam emails as possible while minimizing the number of spam emails falsely labeled as non-spam.
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