Balanced Accuracy & Youden's J Calculator
Average of sensitivity and specificity — the imbalance-proof accuracy, plus Youden's J for optimal threshold selection.
Defaults model a rare-class problem: plain accuracy ~96% looks great, but balanced accuracy ~88% reveals the model misses 20% of positives. Youden's J (0 to 1) is the metric you maximize to pick a probability threshold from a ROC curve.
Formula
About Balanced Accuracy & Youden's J Calculator
Plain accuracy lies on imbalanced data — predict the majority class and score 95% while being useless. Balanced accuracy fixes it by averaging sensitivity and specificity, giving each class equal say regardless of how many samples it has. This calculator computes it from your confusion counts alongside the naive accuracy (so you can see the gap) and Youden's J statistic, the quantity you maximize to choose an optimal classification threshold from a ROC curve. The default scenario shows the trap: 96% accuracy hiding a 20% miss rate on the minority class.
How to use Balanced Accuracy & Youden's J Calculator
- 1Enter your values into Balanced Accuracy & Youden's J Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Balanced Accuracy & Youden's J Calculator?
- ✓Computes Balanced Accuracy & Youden's J instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: balanced accuracy = (sensitivity + specificity) / 2.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
When should I use balanced accuracy instead of accuracy?+
Whenever your classes are imbalanced and you care about all of them. Balanced accuracy reduces to plain accuracy on balanced data, so there's little downside to defaulting to it. It's especially standard in medical, fraud and anomaly settings where the rare class is the whole point.
What is Youden's J used for?+
Threshold selection. A probabilistic classifier needs a cutoff to turn scores into yes/no; Youden's J (sensitivity + specificity − 1) is maximized at the ROC point furthest from the chance diagonal. Picking the threshold that maximizes J gives a balanced operating point when you have no cost information favoring one error type.
How does balanced accuracy relate to ROC-AUC?+
Balanced accuracy is the performance at ONE threshold; ROC-AUC summarizes performance across ALL thresholds. Balanced accuracy at the optimal-J threshold is a single concrete operating point on the curve. Report AUC to characterize the model, balanced accuracy to characterize a deployed decision rule.
Can balanced accuracy be below 50%?+
Yes — it means the model is worse than random on average across classes (e.g. it systematically flips a class). 50% is the chance baseline (Youden's J = 0). Below that signals a bug like inverted labels or a threshold so extreme it sacrifices one class entirely.
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