January 9, 2021
Bagging is a combination of two words – bootstrap and aggregation. It leverages the benefits of both, by bootstrapping it creates different subsets of data, creates a model for each subset an then aggregates the output of each algorithm to make the final prediction. It should be noted that in bagging only one algorithms is used to create all the models. The model outputs are different from one another owing to difference in data fed to them and not because of difference in algorithm used.
In classification problems the voting is used as aggregation method. While in regression problems mean is used as method of aggregation.
by : Monis Khan
Quick Summary:
Bagging is a combination of two words – bootstrap and aggregation. It leverages the benefits of both, by bootstrapping it creates different subsets of data, creates a model for each subset an then aggregates the output of each algorithm to make the final prediction. It should be noted that in bagging only one algorithms is […]