What is imbalanced data in machine learning?

What is imbalanced data in machine learning?

Imbalanced data refers to datasets where the classes are not represented equally, leading to biased models.

Can you give me an example of imbalanced data?

Sure! A common example is a fraud detection dataset where 95% of transactions are legitimate and only 5% are fraudulent.

Why is imbalanced data a problem?

It can lead to models that are biased towards the majority class, resulting in poor performance on the minority class.

How can I handle imbalanced data?

You can use techniques like resampling, synthetic data generation, or adjusting class weights to address the imbalance.