পদ্ধতির তুলনা করুন

	Semi-supervised Random Forest	গ্রেডিয়েন্ট বুস্টিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2009	2001
প্রবর্তক	Leistner, C., Saffari, A., Santner, J., & Bischof, H.	Friedman, J. H.
ধরন	Semi-supervised ensemble classifier	Ensemble (sequential boosting of decision trees)
মৌলিক উৎস	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
অপর নাম	SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
সম্পর্কিত	3	5
সারসংক্ষেপ	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.