What are the best evaluation metrics for imbalanced data?

What are the best evaluation metrics for imbalanced data?

For imbalanced data, metrics like F1-score, precision, recall, and AUC-ROC are more informative than accuracy.

Can you explain why accuracy isn't a good metric in this case?

Accuracy can be misleading in imbalanced datasets because it may give a false sense of performance by favoring the majority class.

How do I calculate the F1-score?

The F1-score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).

Are there any tools or libraries that can help with these calculations?

Yes, libraries like scikit-learn in Python provide built-in functions to calculate these metrics easily.