Empirical Analysis of the AdaBoost's Error Bound
2023-02-02Code Available0· sign in to hype
Arman Bolatov, Kaisar Dauletbek
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- github.com/armanbolatov/adaboost_error_boundOfficialIn papernone★ 1
Abstract
Understanding the accuracy limits of machine learning algorithms is essential for data scientists to properly measure performance so they can continually improve their models' predictive capabilities. This study empirically verified the error bound of the AdaBoost algorithm for both synthetic and real-world data. The results show that the error bound holds up in practice, demonstrating its efficiency and importance to a variety of applications. The corresponding source code is available at https://github.com/armanbolatov/adaboost_error_bound.