What are the differences between bagging and boosting in machine learning?

What are the differences between bagging and boosting in machine learning?

Bagging and boosting are both ensemble techniques used to improve model performance. Bagging reduces variance by averaging predictions from multiple models trained on different subsets of the data, while boosting reduces bias by sequentially training models, where each new model focuses on the errors made by 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 AdaBoost, which combines weak classifiers to create a strong classifier by focusing on misclassified instances.

When should I use bagging over boosting?

You should use bagging when your model is prone to overfitting, as it helps to stabilize predictions. Boosting is preferable when you want to improve the accuracy of a weak model, especially when the data is noisy.

Are there any downsides to using these methods?

Yes, bagging can be computationally expensive due to training multiple models, while boosting can be sensitive to noisy data and outliers, potentially leading to overfitting.