Σύγκριση μεθόδων

	Μετασχηματιστής (Επεξεργασία Φυσικής Γλώσσας)	Λογιστική Παλινδρόμηση	Τυχαίο Δάσος	XGBoost
Πεδίο	Βαθιά Μάθηση	Ερευνητική Στατιστική	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Process / pipeline	Machine learning	Machine learning
Έτος προέλευσης	2017	1958	2001	2016
Δημιουργός	Vaswani, A. et al.	David Roxbee Cox	Breiman, L.	Chen, T. & Guestrin, C.
Τύπος	Attention-based deep neural network	Method	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Συναφείς	4	3	4	5
Σύνοψη	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.	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.