Bandingkan metode

	Pencarian Arsitektur Neural	XGBoost
Bidang	Pembelajaran Mendalam	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2017	2016
Pencetus	Zoph, B. & Le, Q.V.	Chen, T. & Guestrin, C.
Tipe	Automated architecture optimization (deep learning)	Ensemble (gradient-boosted decision trees)
Sumber perintis	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	XGBoost, extreme gradient boosting, scalable tree boosting
Terkait	5	5
Ringkasan	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.