How Does Imbalanced Data Affect Model Performance?
Imbalanced data can lead to several issues in model performance:
- Bias Towards Majority Class: Models may predict the majority class more often, leading to a high number of false negatives. - Misleading Accuracy: High overall accuracy might be misleading if the model is predominantly predicting the majority class correctly while failing to predict the minority class. - Poor Generalization: Models trained on imbalanced data may not generalize well to new, unseen data, especially for the minority class.