Balanced Accuracy Calculator
Calculate balanced accuracy, the average of sensitivity and specificity, ideal for imbalanced datasets.
Inputs
Balanced Accuracy
0.80%
Sensitivity
0.62%
Specificity
0.98%
Step by step
Sensitivity (recall): TP / (TP + FN)
80 ÷ (80 + 50)
= 0.6154
Specificity: TN / (TN + FP)
850 ÷ (850 + 20)
= 0.9770
Balanced accuracy: (sensitivity + specificity) / 2
(0.6154 + 0.9770) ÷ 2
= 0.7962
How it works
Balanced accuracy averages the model's performance on each class separately: bal_acc = (sensitivity + specificity) / 2, where sensitivity is the true positive rate and specificity is the true negative rate. Because it weighs both classes equally regardless of how many examples each contains, balanced accuracy avoids the trap of standard accuracy on imbalanced datasets — a model that always predicts the majority class scores around 0.5 on balanced accuracy, correctly revealing it has no real skill.
Formula
balanced_accuracy = (sensitivity + specificity) / 2
- Sensitivity
- TP / (TP + FN)
- Specificity
- TN / (TN + FP)
Frequently Asked Questions
How does balanced accuracy differ from regular accuracy?
Regular accuracy weighs every prediction equally, so it's dominated by the majority class on imbalanced data; balanced accuracy instead averages the per-class recall rates, giving each class equal importance regardless of its size.
What does a balanced accuracy of 0.5 mean?
A balanced accuracy of 0.5 (50%) indicates the classifier performs no better than random guessing when both classes are weighted equally — a common result for a model that just predicts the majority class every time.
Is balanced accuracy the same as MCC?
No — they're both robust to class imbalance but computed differently; MCC uses a correlation-style formula incorporating all four confusion matrix cells simultaneously, while balanced accuracy is simply the arithmetic mean of sensitivity and specificity.