What is the geometric mean (G-mean) in classification metrics?
mediumAnswer
- G-mean = sqrt(sensitivity * specificity).
- Only high when both true positive and true negative rates are high — a single point going to zero drags the metric to zero.
- Useful for imbalanced problems where you want a classifier that performs well on both classes, not one that trivially favors the majority.
- Common in medical diagnostics.
Check yourself — multiple choice
- Arithmetic mean of accuracy and F1
- G-mean = sqrt(sensitivity × specificity) — zero if either class is ignored
- The same as F1
- Only used for regression
sqrt(sensitivity × specificity) — punishes imbalanced-class performance.
#metrics#classification#imbalance
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