What are some real-world examples of imbalanced data?

What are some real-world examples of imbalanced data?

Imbalanced data occurs when the classes in a dataset are not represented equally. For example, in fraud detection, fraudulent transactions are much rarer than legitimate ones.

Can you give me another example?

Sure! In medical diagnosis, a dataset might have many healthy patients but only a few with a rare disease, leading to imbalanced data.

How does this affect machine learning models?

Imbalanced data can lead to biased models that perform poorly on the minority class, as they may not learn enough from the limited examples.

What techniques can be used to handle imbalanced data?

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