What is the difference between bagging and boosting?

What is the difference between bagging and boosting?

Bagging and boosting are both ensemble learning techniques, but they differ in their approach. Bagging reduces variance by averaging predictions from multiple models trained independently, while boosting reduces bias by sequentially training models, where each new model focuses on the errors of the previous ones.

Can you give me an example of each?

Sure! An example of bagging is the Random Forest algorithm, which builds multiple decision trees and averages their predictions. An example of boosting is the AdaBoost algorithm, which combines weak classifiers to create a strong classifier by focusing on misclassified instances.

Which one is better for accuracy?

It depends on the dataset and the problem at hand. Boosting often achieves higher accuracy by reducing bias, but it can be more prone to overfitting. Bagging is generally more robust and can perform better on noisy datasets.

Are there any scenarios where one is preferred over the other?

Yes, bagging is preferred when you have a high-variance model and want to stabilize predictions, while boosting is preferred when you need to improve accuracy on a complex dataset with many features.