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Mean Average Precision

Precision and Recall

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Precision

Precision is the ratio of true positives (TP) to the sum of true positives and false positives (FP):

$$ \text{Precision}=\frac{TP}{TP+FP} $$

Precision tells you how many of the positive predictions made by the model were actually correct.

Recall

Recall (or sensitivity) is the ratio of true positives to the sum of true positives and false negatives (FN):

$$ \text{Recall}=\frac{TP}{TP+FN} $$

Recall tells you how many of the actual positive cases were correctly identified by the model.

Precision-Recall(PR) Curve & Average Precision

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