Comparer des méthodes

	CatBoost semi-supervisé	Forêt aléatoire semi-supervisée
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018 (CatBoost); semi-supervised learning framework predates 2006	2009
Auteur d'origine	Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.	Leistner, C., Saffari, A., Santner, J., & Bischof, H.
Type	Semi-supervised ensemble (gradient boosting)	Semi-supervised ensemble classifier
Source fondatrice	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (NeurIPS), 31.	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.
Alias	SSL CatBoost, semi-supervised gradient boosting with CatBoost, CatBoost with unlabeled data, pseudo-label CatBoost	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest
Apparentées	5	3
Résumé	Semi-supervised CatBoost applies CatBoost's ordered gradient boosting framework to settings where only a fraction of training instances carry labels, leveraging unlabeled data through pseudo-labeling or consistency-based strategies to improve model accuracy beyond what labeled data alone would allow.	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.