So sánh phương pháp

	Rừng ngẫu nhiên	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2001	1970s–2006 (formalized)
Người khởi xướng	Breiman, L.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Ensemble (bagging of decision trees)	Learning paradigm
Công trình gốc	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	4	5
Tóm tắt	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.