What is oversampling in machine learning?

Can you explain what oversampling is in machine learning?

Sure! Oversampling is a technique used to balance imbalanced datasets by increasing the number of instances in the minority class.

Why is oversampling necessary in machine learning?

It's necessary because imbalanced datasets can lead to biased models that perform poorly on the minority class, affecting overall accuracy.

What are some common methods of oversampling?

Common methods include Random Oversampling, SMOTE (Synthetic Minority Over-sampling Technique), and ADASYN.

Are there any downsides to using oversampling?

Yes, oversampling can lead to overfitting since it replicates existing data points or creates synthetic ones, which may not generalize well.