## [Precision](/precision-0) Definition In information retrieval and classification, the fraction of items labeled as positive by a system that are actually true positives; also called positive predictive value. ## Principle Principle Quantify the trustworthiness of positive predictions by dividing true positives by all predicted positives; emphasizes correctness among predicted positives rather than coverage of all actual positives. ## Demonstration Demonstration A spam filter marks 50 emails as spam; 40 of those are truly spam, so precision = 40/50 = 0.8. ## Misapplication Misapplication Optimizing solely for precision can produce a system that labels very few items as positive (to avoid false positives), yielding poor recall and missing many actual positives. ## Consequence Consequence High precision reduces the rate of false alarms and is appropriate when the cost of false positives is high; it supports decisions where positive predictions must be reliable. ## Reversal Reversal Prioritizing recall over precision (e.g., by lowering the decision threshold) increases detection of positives but typically reduces precision because more false positives are included. ## Boundary Boundary Defined for classification tasks with a clear positive class and binary or thresholded outputs; not directly applicable to multi-class settings without per-class definition or to regression without discretization. ## Semantic Tension Semantic Tension Often contrasted with accuracy and recall; precision measures quality of positives while recall measures quantity of true positives captured—balancing both is necessary for reliable classifiers. ## Synthesis Synthesis Precision measures the purity of predicted positives: the proportion of predicted positives that are correct. Use it when false positives are costly, and combine with recall or F-score to understand overall performance.