Togaware DATA MINING
Desktop Survival Guide
by Graham Williams
Google


Bagging:
Meta Algorithm

Image under-construction
Bagging is a variance reduction method for model building. That is, through building multiple models from samples of the training data, the aim is to reduce the variance. Bagging is a technique generating multiple training sets by sampling with replacement from the available training data. In an ideal world we can eliminate variance due to a particular choice of training set by combining models that are built from each training set of size N. In practise only one training set is available. By sampling with replacement from the training set to form new training sets, bagging simulates the ideal situation. Bagging is also known as bootstrap aggregating. See See Chapter 13.1



Copyright © 2004-2010 Togaware Pty Ltd
Support further development through the purchase of the PDF version of the book.
The PDF version is a formatted comprehensive draft book (with over 800 pages).
Brought to you by Togaware. This page generated: Sunday, 22 August 2010