What is the impact of class imbalance on model accuracy?

What is the impact of class imbalance on model accuracy?

Class imbalance can significantly affect model accuracy by biasing the model towards the majority class, leading to poor performance on the minority class.

Can you explain why this happens?

When a dataset has a class imbalance, the model tends to learn more about the majority class, as it has more examples to learn from, which can result in misclassifying the minority class.

How can we address class imbalance?

There are several techniques to address class imbalance, such as resampling the dataset, using different algorithms, or applying cost-sensitive learning.

What are some examples of resampling techniques?

Common resampling techniques include oversampling the minority class, undersampling the majority class, and using synthetic data generation methods like SMOTE.