APA (7th ed.) Citation

Ramdani, F., & Furqon, M. T. (2022). The simplicity of XGBoost algorithm versus the complexity of Random Forest, Support Vector Machine, and Neural Networks algorithms in urban forest classification [version 1; peer review: 1 approved]. F1000 research, 11, 1069. https://doi.org/10.12688/f1000research.124604.1

Chicago Style (17th ed.) Citation

Ramdani, Fatwa, and Muhammad Tanzil Furqon. "The Simplicity of XGBoost Algorithm Versus the Complexity of Random Forest, Support Vector Machine, and Neural Networks Algorithms in Urban Forest Classification [version 1; Peer Review: 1 Approved]." F1000 Research 11 (2022): 1069. https://doi.org/10.12688/f1000research.124604.1.

MLA (9th ed.) Citation

Ramdani, Fatwa, and Muhammad Tanzil Furqon. "The Simplicity of XGBoost Algorithm Versus the Complexity of Random Forest, Support Vector Machine, and Neural Networks Algorithms in Urban Forest Classification [version 1; Peer Review: 1 Approved]." F1000 Research, vol. 11, 2022, p. 1069, https://doi.org/10.12688/f1000research.124604.1.