What are common mistakes when dealing with imbalanced data?

What are common mistakes when dealing with imbalanced data?

Some common mistakes include ignoring the imbalance, using inappropriate evaluation metrics, and not applying proper resampling techniques.

Can you explain why ignoring the imbalance is a mistake?

Ignoring the imbalance can lead to misleading results, as the model may perform well on the majority class but poorly on the minority class.

What evaluation metrics should I use instead?

Instead of accuracy, consider using metrics like precision, recall, F1-score, or the area under the ROC curve.

How can I effectively resample my data?

You can use techniques like oversampling the minority class, undersampling the majority class, or generating synthetic samples with SMOTE.

Are there any tools that can help with this?

Yes, libraries like imbalanced-learn in Python provide various methods for resampling and handling imbalanced datasets.