قارن الطرق

	LightGBM شبه المُشرف عليه	الغابات العشوائية شبه المُشرف عليها
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2017–2019	2009
صاحب الطريقة	Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
النوع	Semi-supervised gradient boosting ensemble	Semi-supervised ensemble classifier
المصدر التأسيسي	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.	Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.
الأسماء البديلة	SSL-LightGBM, pseudo-label LightGBM, self-training LightGBM, semi-supervised GBDT	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
ذات صلة	4	3
الملخص	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 Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.