Model Precision

Model precision is a measure of how accurately a model identifies positive cases. It is the ratio of true positive predictions to all positive predictions made by the model (true positives plus false positives). In simpler terms, it tells us, out of all the times the model said "positive," how often it was right. High precision means the model is good at avoiding false alarms and only labels cases as positive when it's very confident. Precision is especially important when false positives are costly or problematic.