What are bagging techniques in machine learning?

What are bagging techniques in machine learning?

Bagging, or Bootstrap Aggregating, is an ensemble method that improves the stability and accuracy of machine learning algorithms by combining multiple models.

How does bagging improve model performance?

Bagging reduces variance by training multiple models on different subsets of the data and averaging their predictions, which helps to mitigate overfitting.

Can you give me an example of a bagging algorithm?

Sure! A common example of a bagging algorithm is the Random Forest, which builds multiple decision trees and merges their results.

What are the advantages of using bagging?

The main advantages of bagging include improved accuracy, reduced overfitting, and better performance on unstable models.

Are there any disadvantages to bagging?

Yes, bagging can be computationally expensive and may not perform well with small datasets, as it relies on creating multiple models.