What are some examples of data imbalance in machine learning?

What are some examples of data imbalance in machine learning?

Data imbalance occurs when the classes in a dataset are not represented equally. For example, in a medical diagnosis dataset, there might be 95% healthy patients and only 5% with a specific disease.

Can you give me another example?

Sure! In fraud detection, you might have a dataset with 99% legitimate transactions and only 1% fraudulent ones, leading to imbalance.

How does data imbalance affect model performance?

It can lead to biased models that perform well on the majority class but poorly on the minority class, resulting in high false negative rates.

What techniques can be used to handle data imbalance?

Common techniques include resampling methods like oversampling the minority class, undersampling the majority class, and using algorithms that are robust to imbalance.