Compară metode

	LightGBM semi-supervizat	XGBoost semi-supervizat
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2017–2019	2016–2018
Autorul original	Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research	Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors
Tip	Semi-supervised gradient boosting ensemble	Ensemble (semi-supervised gradient boosting)
Sursa seminală	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.	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.
Denumiri alternative	SSL-LightGBM, pseudo-label LightGBM, self-training LightGBM, semi-supervised GBDT	SS-XGBoost, semi-supervised gradient boosting, pseudo-label XGBoost, label-propagation XGBoost
Înrudite	4	4
Rezumat	Semi-supervised LightGBM combines LightGBM's highly efficient gradient boosting framework with semi-supervised strategies — most commonly pseudo-labeling or self-training — to exploit large pools of unlabeled data alongside a smaller labeled set, improving predictive performance when obtaining labels is costly or time-consuming.	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.