Desktop Survival Guide
by Graham Williams

Meta Algorithm

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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


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