Machine Learning for OpenCV 4 - Second Edition

Understanding averaging ensembles

Averaging methods have a long history in machine learning and are commonly applied to fields such as molecular dynamics and audio signal processing. Such ensembles are typically seen as exact replicas of a given system.

An averaging ensemble is essentially a collection of models that train on the same dataset. Their results are then aggregated in a number of ways.

One common method involves creating multiple model configurations that take different parameter subsets as input. Techniques that take this approach are referred to collectively as bagging methods.

Bagging methods come in many different flavors. However, they typically only differ in the way they draw random subsets of the training set:

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