Binary Classification: F1 Score vs. Matthews Correlation Coefficient

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The F1 score and the Matthews Correlation Coefficient (MCC) are both metrics commonly used in binary classification scenarios. While both metrics aim to capture the balance between precision and recall, they differ in their formulations and emphasize different aspects of model performance. Let’s compare the F1 score with the Matthews Correlation Coefficient, highlighting their respective strengths and considerations:

I. F1 Score:

1. Formula:

• The F1 score is the harmonic mean of precision and recall.
• F1 = 2 * [(Precision + Recall) / (Precision * Recall​)]

2. Strengths:

• F1 score provides a single metric that balances precision and recall.
• Particularly useful when there is an imbalance between the classes, and both false positives and false negatives need to be considered.

3. Considerations:

• It does not provide insights into the distribution of false positives and false negatives separately.
• It may not be suitable for scenarios where the consequences of false positives and false negatives are significantly different.

II. Matthews…