Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Bolatov, Arman, Dauletbek, Kaisar
Institution: Nazarbayev University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4319165755