Confusion Matrix Metrics
Comprehensive binary classifier evaluation: Matthews Correlation Coefficient, balanced accuracy, informedness, markedness, Cohen's Kappa, and all standard metrics.
Results
What is it?
This calculator computes the full suite of binary classification metrics. The Matthews Correlation Coefficient (MCC) is often considered the most reliable single metric — it accounts for all four confusion matrix values and ranges from -100% (perfect inverse) to +100% (perfect prediction), with 0 being random chance.
How to use
Enter the four cells of your confusion matrix: True Positives, False Positives, True Negatives, and False Negatives. All metrics are computed automatically.
Example scenario
TP=90, FP=10, TN=85, FN=15. Accuracy=87.5%, F1=90%, MCC=75%, Balanced Accuracy=87.5%, Kappa=75%. The MCC and Kappa give a more honest picture on slightly imbalanced data.
Pro tip
MCC > 0.7 is generally considered good. Balanced Accuracy is preferred over plain accuracy on class-imbalanced datasets. Cohen's Kappa > 0.8 indicates strong agreement beyond chance.