Bagging vs Boosting vs Stacking In Machine Learning
Bagging, boosting, and stacking are three ensemble learning techniques used to improve model performance. Bagging involves training multiple models independently on random subsets of data and then combining their predictions through a majority vote. Boosting focuses on correcting the errors made by previous weak models in a sequence to create a stronger model. Stacking combines multiple models by training a meta-model, which takes model predictions as input and outputs the final prediction....