Bagging, also known as Bootstrap Aggregating, combines multiple models trained on different subsets of data. The basic concept of bagging is to reduce variance by averaging out individual model errors. Each model is trained independently and has equal weight in the final decision.
Boosting, on the other hand, trains models sequentially, focusing on the error made by the previous model. The objective of boosting is to reduce both bias and variance by correcting misclassifications of the previous model. Models are weighted based on accuracy, with better-accuracy models having a higher weight.
Bagging combines multiple models trained on different subsets of data, while boosting trains models sequentially, focusing on the error made by the previous model.
Bagging aims to reduce variance, while boosting aims to reduce both bias and variance.
Bagging uses Bootstrap to create subsets of the data, while boosting re-weights the data based on the error from the previous model.
In bagging, each model has equal weight in the final decision, while in boosting, models are weighted based on accuracy.
Bagging: Random Forest
Boosting: AdaBoost, XGBoost, Gradient Boosting Mechanism
Bagging: Best for high variance, low bias models.
Boosting: Effective when the model needs to be adaptive to errors, suitable for both bias and variance errors.
Bagging: Reduces overfitting, decreases model variance, improves stability, handles high variability, and is parallelizable. However, it might not significantly improve the performance of an already stable model.
Boosting: Improves accuracy, reduces bias and variance, and is effective for weak learners. However, it can be prone to overfitting if the number of models or iterations is high.
Bagging and boosting are two ensemble techniques used in machine learning to improve model performance. While bagging combines multiple models trained on different subsets of data to reduce variance, boosting trains models sequentially to reduce both bias and variance.