Vertaile menetelmiä

	Graafiverkko (Graph Attention Network, GAT)	Logistinen regressio	Random Forest
Tieteenala	Syväoppiminen	Tutkimuksen tilastomenetelmät	Koneoppiminen
Menetelmäperhe	Machine learning	Process / pipeline	Machine learning
Syntyvuosi	2018	1958	2001
Kehittäjä	Veličković, P. et al.	David Roxbee Cox	Breiman, L.
Tyyppi	Graph neural network (attention-based)	Method	Ensemble (bagging of decision trees)
Alkuperäislähde	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	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.
Rinnakkaisnimet	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liittyvät	4	3	4
Tiivistelmä	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).	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.