Methoden vergelijken

	Graph Attention Network	Logistische Regressie	XGBoost
Vakgebied	Deep learning	Onderzoeksstatistiek	Machine learning
Familie	Machine learning	Process / pipeline	Machine learning
Jaar van ontstaan	2018	1958	2016
Grondlegger	Veličković, P. et al.	David Roxbee Cox	Chen, T. & Guestrin, C.
Type	Graph neural network (attention-based)	Method	Ensemble (gradient-boosted decision trees)
Oorspronkelijke bron	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Aliassen	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Verwant	4	3	5
Samenvatting	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.	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.