Model Accuracy Calculator
Why Accuracy Alone Can Be Misleading
A classifier that predicts "no fraud" on every single transaction can post 99% accuracy on a dataset where fraud is rare — and still be useless. This calculator takes the four raw counts from a confusion matrix and derives accuracy alongside precision, recall, specificity, and F1 score, so a model's performance can be judged on more than one number.
The Formulas
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP)
Recall (Sensitivity) = TP / (TP + FN)
Specificity = TN / (TN + FP)
F1 Score = 2 × (Precision × Recall) / (Precision + Recall)
Precision = TP / (TP + FP)
Recall (Sensitivity) = TP / (TP + FN)
Specificity = TN / (TN + FP)
F1 Score = 2 × (Precision × Recall) / (Precision + Recall)
Worked Example: The Accuracy Paradox
| Metric | Calculation | Result |
|---|---|---|
| Accuracy | (85 + 850) / 1,000 | 93.50% |
| Precision | 85 / (85 + 15) | 85.00% |
| Recall | 85 / (85 + 50) | 62.96% |
| Specificity | 850 / (850 + 15) | 98.27% |
| F1 Score | 2 × (0.85 × 0.6296) / (0.85 + 0.6296) | 72.34% |
Accuracy alone (93.5%) looks strong, but recall of only 63% means the model is missing more than a third of the actual positive cases — a gap accuracy doesn't reveal on its own.
Where This Matters
- Imbalanced datasets — fraud detection, disease screening, and spam filtering all have a rare positive class, where accuracy is the least informative metric available.
- Choosing between precision and recall — a spam filter favors precision (don't flag real email), while a cancer screening tool favors recall (don't miss a real case).
- Model comparison — F1 score gives a single balanced number when both false positives and false negatives carry real cost.
How to Use This Calculator
- Enter True Positives — correctly predicted positive cases.
- Enter True Negatives — correctly predicted negative cases.
- Enter False Positives — negative cases incorrectly predicted positive.
- Enter False Negatives — positive cases incorrectly predicted negative.
- Select Calculate to see accuracy, precision, recall, specificity, and F1 score.
Related Calculations
Check whether a model is overfitting with the Overfitting Ratio Calculator, or size the network behind these predictions with the Neural Network Parameter Calculator.