Phi coefficient

The Matthews correlation coefficient (MCC) is a balanced metric that accounts for true and false positives and negatives, making it effective even when classes are imbalanced.

MCC considers all four combinations of the predicted and actual binary classifications: true positives, true negatives, false positives, and false negatives. This comprehensive approach ensures that the performance metric reflects the actual effectiveness of a binary classification model.

Unlike other metrics that may favor the majority class, MCC provides a balanced view of model performance across both classes. This makes it particularly useful for evaluating models in scenarios where class imbalance is present.