Porovnat metody

	Random Forest	Multi-Head Self-Attention
Obor	Strojové učení	Hluboké učení
Rodina	Machine learning	Machine learning
Rok vzniku	2001	2017
Tvůrce	Breiman, L.	Vaswani, A. et al.
Typ	Ensemble (bagging of decision trees)	Attention mechanism (Transformer core)
Původní zdroj	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Další názvy	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention
Příbuzné	4	5
Shrnutí	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.