Võrdle meetodeid

	Transformer (NLP)	Autoencoder	Logistiline regressioon	XGBoost
Valdkond	Süvaõpe	Süvaõpe	Uurimisstatistika	Masinõpe
Perekond	Machine learning	Machine learning	Process / pipeline	Machine learning
Tekkeaasta	2017	2006	1958	2016
Looja	Vaswani, A. et al.	Hinton, G.E. & Salakhutdinov, R.R.	David Roxbee Cox	Chen, T. & Guestrin, C.
Tüüp	Attention-based deep neural network	Neural network (encoder-decoder)	Method	Ensemble (gradient-boosted decision trees)
Algallikas	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	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.
Rööpnimetused	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Seotud	4	4	3	5
Kokkuvõte	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.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	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.