What are the benefits of oversampling in machine learning?

What are the benefits of oversampling in machine learning?

Oversampling helps to balance class distribution, improving model performance on minority classes.

Can you explain how it improves model accuracy?

By providing more examples of the minority class, oversampling allows the model to learn better patterns, leading to higher accuracy.

Are there any downsides to oversampling?

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

What techniques are commonly used for oversampling?

Common techniques include SMOTE (Synthetic Minority Over-sampling Technique) and random oversampling.

How do I decide if I should use oversampling?

Evaluate your dataset's class distribution and model performance; if the minority class is underrepresented, consider oversampling.