Model Performance Metrics Calculator
Calculate accuracy, precision, recall, F1 score, MCC, AUC, and other classification performance metrics.
Inputs
Results
Accuracy
87.5%
Precision
89.47%
Recall (Sensitivity)
85%
F1 Score
87.18%
Matthews Correlation Coefficient75.09
AUC (Area Under Curve)87.5
How to Use This Calculator
- Enter the confusion matrix values: true positives, false positives, true negatives, false negatives.
- Review the derived metrics: accuracy, precision, recall, F1 score, and specificity.
- Set the positive class prevalence to see how threshold changes affect precision-recall tradeoff.
- For imbalanced datasets, prioritize F1 score or AUC-ROC over raw accuracy.
- Compare metrics across models to select the best performer on your evaluation criteria.
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Formula
F1 = 2 × (Precision × Recall) / (Precision + Recall)Related Calculators
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