Boosting is a way of making machines better at predicting in a more accurate manner by reducing bias and variance in supervised learning.
Robert Schapire and Yoav Freund were the first to create boosting algorithms, a list of words required to logically solve a problem, in 1988/99.
These two created the algorithms on the premise of optimising machine learning, with Freund’s algorithm stating that boosting would be conducted by a majority in which many ‘weaker’ learning machines were combined to create a better-performing one.
