What are the limitations of bagging in machine learning?

What are the limitations of bagging in machine learning?

Bagging can reduce variance but may not improve bias. It also requires a lot of data and can be computationally expensive.

Can bagging be used with any type of model?

Yes, bagging can be applied to various models, but it works best with high-variance models like decision trees.

How does bagging compare to boosting?

Bagging focuses on reducing variance by averaging predictions, while boosting aims to reduce bias by sequentially correcting errors.

Are there scenarios where bagging is not effective?

Yes, bagging may not be effective when the base model is already low-variance or when the dataset is small.

What are some common algorithms that use bagging?

Common algorithms include Random Forests and Bagged Decision Trees, which leverage the bagging technique for improved performance.