 - Sampling with replacement the original dataset to get k different dataset and feed them to k different models. This will ensure the randomness, which implies the independent errors in each model. By averaging the test output of each models, we obtain output with low error.
- Prediction of ensemble is given by arithmetic mean.

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- Bagging is short for bootstrap aggregating.

- Bagging is a technique for reducing generalization error by combining several machine learning models.

- Bagging employs model averaging.

- This is powerful method of regularization, which is widely used in machine learning contest. However, it is quite impractical, since the computational cost of training several models in expensive.
 


Bagging in Deep Learning

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

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