Сравнение на методи

	Трансформър (обработка на естествен език)	Логистична регресия	XGBoost
Област	Дълбоко обучение	Статистика за изследвания	Машинно обучение
Семейство	Machine learning	Process / pipeline	Machine learning
Година на възникване	2017	1958	2016
Създател	Vaswani, A. et al.	David Roxbee Cox	Chen, T. & Guestrin, C.
Тип	Attention-based deep neural network	Method	Ensemble (gradient-boosted decision trees)
Основополагащ източник	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Други названия	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Свързани	4	3	5
Резюме	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.