Classification Metrics

Accuracy

Proportion of correct predictions:

$\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$

Precision

Proportion of positive predictions that are correct:

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

Answers: "Of all predicted positives, how many are truly positive?"

Recall (Sensitivity)

Proportion of actual positives correctly identified:

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

Answers: "Of all actual positives, how many did we find?"

Notation

• $TP$ = True Positives
• $TN$ = True Negatives
• $FP$ = False Positives
• $FN$ = False Negatives

Trade-off

High precision = fewer false alarms High recall = fewer missed cases Often inversely related.