When should I use bagging in machine learning?

When should I use bagging in machine learning?

Bagging, or Bootstrap Aggregating, is useful when you want to reduce variance and improve model stability.

Can you give me an example of a situation where bagging is beneficial?

Sure! Bagging is particularly effective with high-variance models like decision trees, especially in noisy datasets.

What are some popular algorithms that use bagging?

Random Forest is a well-known algorithm that utilizes bagging to enhance accuracy and control overfitting.

Are there any downsides to using bagging?

Yes, bagging can increase computational cost and may not perform well with low-variance models.