Bagging = bootstrapping = sampling with replacement
Fitting only one algorithm can be misleading. With more samples, we can see how our models handle uncertainty. Bootstrap samples (bagging) provide a reliable estimate of model performance on unseen data. However, resampling methods can be computationally expensive.
The reason this works so well, is that despite they models are not trained on the full dataset and are thus not as accurate as they could be, the errors they make are uncorrelated. That means that upon taking the average of the models, the errors cancel out (the average is zero) and we obtain a good result - better than should the prediction come from a single model.
Bagging = bootstrapping = sampling with replacement
https://www.youtube.com/watch?v=Xz0x-8-cgaQ&list=PLblh5JKOoLUIcdlgu78MnlATeyx4cEVeR&index=140&ab_channel=StatQuestwithJoshStarmer