What is the impact of oversampling on machine learning models?

What is the impact of oversampling on machine learning models?

Oversampling is a technique used to address class imbalance in datasets. It can improve model performance by providing more examples of the minority class.

Can you explain how it affects model accuracy?

Certainly! Oversampling can lead to higher accuracy for the minority class, but it may also increase the risk of overfitting, as the model might learn noise from duplicated samples.

Are there any downsides to using oversampling?

Yes, some downsides include potential overfitting, increased training time, and the possibility of creating a biased model if not done carefully.

What are some alternatives to oversampling?

Alternatives include undersampling the majority class, using synthetic data generation techniques like SMOTE, or employing ensemble methods that handle imbalance better.