How do I evaluate models with imbalanced data?

How do I evaluate models with imbalanced data?

Evaluating models with imbalanced data can be challenging. You might want to use metrics like precision, recall, and F1-score instead of accuracy.

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

The F1-score is the harmonic mean of precision and recall. It's important because it provides a balance between the two, especially in imbalanced datasets.

Are there any techniques to handle imbalanced data?

Yes, techniques like resampling (oversampling the minority class or undersampling the majority class), using synthetic data generation methods like SMOTE, or applying cost-sensitive learning can help.

Can you recommend any libraries for implementing these techniques?

Sure! Libraries like Scikit-learn, imbalanced-learn, and TensorFlow have built-in functions to help with handling imbalanced datasets.