# Bagging

## Online Bagging

*Nikunj C. Oza, Stuart J. Russell:
Online Bagging and Boosting. AISTATS 2001*

### General Description

*Bagging* is an ensemble method that improves the accuracy of a single
classifier. Non-streaming bagging builds a set of base models, training each
model with a bootstrap sample created by drawing random samples with replacement
from the original training set. Each base model's training set contains each of
the original training example a number of times that follows a binomial
distribution. This binomial distribution tends towards a Poisson(1) for large
values. Thus, the online version of the Bagging classifier, instead of using
sampling with replacement, gives each example a weight according to a Poisson(1)
distribution.

### Implementation

In StreamDM, the main algorithm is implemented in `Bagging`

, which
is controlled by the following options:

- Number of classifiers (
**-s**), which sets the number of classifiers used to build the ensemble (10 by default); and - Base classifier (
**-l**), which sets the base classifier to be used to build the members of the ensemble (`SGDLearner`

by default).