Boosting: Revision history

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19 March 2023

  • curprev 15:4415:44, 19 March 2023‎ Walle talk contribs‎ 3,180 bytes +3,180‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Boosting is an ensemble technique in machine learning that aims to improve the predictive accuracy of a model by combining the outputs of multiple weak learners. The concept of boosting was first introduced by Schapire (1990) and Freund (1995), who later developed the widely used algorithm AdaBoost (Adaptive Boosting) with Schapire in 1997. Boosting algorithms work by iteratively adjusting the weights of data point..."
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