How can I evaluate models with imbalanced data?

How can I evaluate models with imbalanced data?

Evaluating models with imbalanced data requires specific techniques. Have you considered using metrics like F1-score or AUC-ROC?

What is the F1-score and why is it important?

The F1-score is the harmonic mean of precision and recall. It's crucial for imbalanced datasets as it balances false positives and false negatives.

Are there any techniques to handle imbalanced data before evaluation?

Yes, techniques like resampling (oversampling the minority class or undersampling the majority class) and using synthetic data generation methods like SMOTE can help.

What about using different algorithms? Do some work better with imbalanced data?

Absolutely! Algorithms like Random Forest and Gradient Boosting can handle imbalanced data better. It's also worth trying ensemble methods.