Explain bagging in Data Science

Bagging is a type of ensemble learning. Bootstrap aggregation is what it’s called. We produce some data with this methodology by employing the bootstrap approach, which uses an existing dataset to produce 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 basic model. Once all the models are trained, when we have to make a prediction, we make predictions using all the trained models and then average the result in the case of regression, and for classification, we choose the result, generated by models, that have the highest frequency.