How can I handle imbalanced data in my machine learning model?

How can I handle imbalanced data in my machine learning model?

You can handle imbalanced data using techniques like resampling, using different algorithms, or adjusting class weights.

What is resampling, and how does it work?

Resampling involves either oversampling the minority class or undersampling the majority class to balance the dataset.

Are there any specific algorithms that work better with imbalanced data?

Yes, algorithms like Random Forest, Gradient Boosting, and certain ensemble methods can perform better with imbalanced datasets.

How do I evaluate the performance of my model on imbalanced data?

You should use metrics like precision, recall, F1-score, and the ROC-AUC curve instead of accuracy.