Porównaj metody

	Transformer (NLP)	Random Forest	XGBoost
Dziedzina	Uczenie głębokie	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2017	2001	2016
Twórca	Vaswani, A. et al.	Breiman, L.	Chen, T. & Guestrin, C.
Typ	Attention-based deep neural network	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Źródło pierwotne	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Inne nazwy	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Pokrewne	4	4	5
Podsumowanie	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.	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.	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.