Bagging.md (587B)
1 # Bagging 2 3 ML D5 4 5 **Definition:** Bagging is the process of training the same model multiple times with a different subset of the data. Bagging is different than pasting as bagging does not take samples that are selected as part of the random sample for training out of the options to add to the random sample. This means one model (predictor) can be trained with multiple instances of the same sample. 6 7 One reason bagging and pasting are good is that they both allow for parallel processing because multiple models do predictions concurrently. The same is also true for model training.