What are the effects of class imbalance on model accuracy?

What are the effects of class imbalance on model accuracy?

Class imbalance can lead to biased models that perform poorly on underrepresented classes, affecting overall accuracy.

Can you explain why this happens?

When one class dominates, the model may learn to predict that class more often, neglecting the minority class.

What can be done to mitigate these effects?

Techniques like resampling, using different evaluation metrics, or employing algorithms designed for imbalanced data can help.

Are there specific metrics I should focus on?

Yes, metrics like precision, recall, and F1-score are more informative than accuracy in imbalanced scenarios.