What are some applications of bagging in machine learning?

What are some applications of bagging in machine learning?

Bagging, or Bootstrap Aggregating, is used in various applications like improving model accuracy, reducing variance, and handling overfitting.

Can you give me an example of a specific algorithm that uses bagging?

Sure! The Random Forest algorithm is a popular example that uses bagging to create multiple decision trees and aggregate their predictions.

How does bagging help in reducing overfitting?

Bagging helps reduce overfitting by averaging the predictions of multiple models, which smooths out the noise and variance in the data.

Are there any limitations to using bagging?

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