59. What is the concept of Bagging in Data science?
Bagging is a type of ensemble learning. Bootstrap aggregation is what it's called. Some data are generated with this methodology by employing the bootstrap method, which uses an existing dataset to generate many samples of the N size. The bootstrapped data is then utilized to train many models simultaneously, resulting in a more robust bagging model than a simple model. For making a prediction, trained models are essentially employed and then average the results in the case of regression, and in the case of classification, the result provided by the models with the highest frequency is selected.
