What are some use cases for bagging in machine learning?

What are some use cases for bagging in machine learning?

Bagging, or Bootstrap Aggregating, is used to improve the stability and accuracy of machine learning algorithms. Common use cases include reducing variance in decision trees, improving performance in ensemble methods, and handling imbalanced datasets.

Can you give me an example of how bagging is applied?

Sure! One popular example is the Random Forest algorithm, which uses bagging to create multiple decision trees from random subsets of the data, leading to better predictive performance.

What types of models benefit the most from bagging?

Models that are prone to overfitting, like decision trees, benefit the most from bagging. It helps to reduce their variance and improve generalization.

Are there any limitations to using bagging?

Yes, while bagging can improve accuracy, it may increase computational cost and may not perform well with models that are already stable, like linear regression.