Methoden vergleichen

	Robust LightGBM	CatBoost
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2017 (LightGBM); robust variants widely adopted 2018–present	2018
Urheber	Ke, G. et al. (LightGBM); robust objectives adapted from Friedman, J. H.	Prokhorenkova, L. et al. (Yandex)
Typ	Ensemble (gradient boosted decision trees with robust loss)	Gradient boosting on decision trees
Wegweisende Quelle	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154.	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.
Aliasnamen	Robust LGBM, LightGBM with Huber loss, outlier-resistant gradient boosting, robust gradient boosted trees	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma
Verwandt	6	5
Zusammenfassung	Robust LightGBM is a gradient boosting framework that pairs Microsoft's highly efficient LightGBM engine with outlier-resistant loss functions — most commonly Huber, quantile, or mean absolute error — so that predictions are not unduly distorted by extreme or erroneous observations. It retains LightGBM's speed and leaf-wise tree growth while providing resistance to heavy-tailed noise in the target variable.	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.