方法对比

	CatBoost	随机森林
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2018	2001
提出者	Prokhorenkova, L. et al. (Yandex)	Breiman, L.
类型	Gradient boosting on decision trees	Ensemble (bagging of decision trees)
开创性文献	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.
别名	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	5	4
摘要	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.