Comparer des méthodes

	CatBoost	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018	2001
Auteur d'origine	Prokhorenkova, L. et al. (Yandex)	Breiman, L.
Type	Gradient boosting on decision trees	Ensemble (bagging of decision trees)
Source fondatrice	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	5	4
Résumé	CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data.	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.