Comparer des méthodes

	Réseau d'attention sur graphe	Forêt Aléatoire	Réseau de neurones récurrent	XGBoost
Domaine	Apprentissage profond	Apprentissage automatique	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning	Machine learning
Année d'origine	2018	2001	1986–1990	2016
Auteur d'origine	Veličković, P. et al.	Breiman, L.	Rumelhart, D. E.; Elman, J. L.	Chen, T. & Guestrin, C.
Type	Graph neural network (attention-based)	Ensemble (bagging of decision trees)	Sequential neural network	Ensemble (gradient-boosted decision trees)
Source fondatrice	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	RNN, Elman network, Jordan network, simple recurrent network	XGBoost, extreme gradient boosting, scalable tree boosting
Apparentées	4	4	3	5
Résumé	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).	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.	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.