Ensembling and Random Forest Part 4 | Bagging & Pasting | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 2019-07-19

Ensembling and Random Forest Description

Ensemble learning, in general, means making a prediction by combining the predictions of different individual models. In this way, the ensemble model tends to be more flexible (less bias) and less data-sensitive (less variance).

Random forest is an ensemble model using bagging as the ensemble method and decision tree as the individual model.

Bagging & Pasting

Instead of running various models on a single dataset, you can use a single model over various random subsets of the dataset. Random sampling with replacement is called Bagging, short for bootstrap aggregating. In case that was difficult to visualize in your head, just imagine disregarding several random entries in the dataset and modelling with the rest. In the case of Pasting, the same process applies, the only difference being that pasting doesn’t allow training instances to be sampled several times for the same predictors. The bootstrap=True parameter specifies the use of Bagging. For pasting, change the argument to bootstrap=False.


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