مقایسهٔ روش‌ها

	XGBoost نیمه‌نظارت‌شده	XGBoost
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	2016–2018	2016
پدیدآور	Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors	Chen, T. & Guestrin, C.
نوع	Ensemble (semi-supervised gradient boosting)	Ensemble (gradient-boosted decision trees)
منبع بنیادین	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
نام‌های دیگر	SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost	XGBoost, extreme gradient boosting, scalable tree boosting
مرتبط	4	5
خلاصه	Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce.	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.