Adaptive Boosting (AdaBoost) is a term from Artificial Intelligence as well as Big Data and Smart Data. AdaBoost is a machine learning method that helps to make particularly accurate predictions. <\/p>\n
Imagine you want to identify faulty products on an assembly line. Individual, simple checkers (small algorithms) that are responsible for this, however, make many mistakes. Adaptive Boosting (AdaBoost) combines many of these simple checkers so that they complement each other. Each individual checker makes only small but important decisions. AdaBoost assigns a weighting to these checkers: checkers that are reliable are given more weight. In the end, the combined decision of all checkers together yields a significantly better result. <\/p>\n
Adaptive Boosting (AdaBoost) allows errors to be detected more quickly and reliably, even if the individual classifiers are not perfect. In practice, AdaBoost is used, for example, to improve spam filters in the e-mail sector or to recognise patterns in large datasets. With Adaptive Boosting, machines become more intelligent \u2013 and make fewer wrong decisions.<\/p>","protected":false},"excerpt":{"rendered":"