Precision (a.k.a. Positive Predictive Value, PPV)#
The share of predicted positives that are truly positive.
Important
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What it is#
Precision (also positive predictive value, PPV) is the fraction of correct positive predictions among all positive predictions:
It answers: of everything the model flagged positive, how much really was?
The trade-off#
Precision counts false positives against you but ignores false negatives — so a model can reach high precision by only flagging the cases it is surest about. It is therefore read together with recall (\(TP/(TP+FN)\)), which counts the positives missed; the F1 score is their harmonic mean.
When it matters#
Precision is the priority when a false positive is costly — a spam filter deleting real mail, a system flagging innocent transactions as fraud — where you would rather miss some positives than raise false alarms.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC) · Precision–Recall AUC (PR-AUC) · Binary Classification · Multiclass AUROC · Classification Probability · Multiclass Classification
Hint
More in Classification & Averaging Metrics
Accuracy · AUC (Area Under the Curve) · Average Precision (AP) · Binary Classification · Classification Probability · Discriminatory Power · F1-score · Gini Coefficient · Harmonic Mean · Log Loss (also called Logarithmic Loss or Cross-Entropy Loss) · Macro AUC · Macro AUROC (Macro-Averaged AUROC) · Macro Averaging · Macro F1
See also
Source article Adapted (context, re-expressed) in our own words from: Precision (a.k.a. Positive Predictive Value, PPV) (insightful-data-lab.com).