مقایسهٔ روش‌ها

	لایت‌جی‌بی‌ام (LightGBM)	تقویت گرادیان منظم‌شده
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2017	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
پدیدآور	Ke, G. et al. (Microsoft)	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
نوع	Gradient boosting decision tree ensemble	Regularized ensemble (additive tree model)
منبع بنیادین	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 (NeurIPS) 30, 3146–3154.	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
نام‌های دیگر	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
مرتبط	5	6
خلاصه	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.