Ensembling and Random Forest Part 5 | Bagging vs 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 vs Pasting

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. Code: The bootstrap=True parameter specifies the use of Bagging. Bagging generally gives much better results than Pasting


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