So sánh phương pháp

	Rừng ngẫu nhiên được điều chuẩn	Cây Quyết định
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2012	1984
Người khởi xướng	Deng, H. & Runger, G.	Breiman, Friedman, Olshen & Stone
Loại	Regularized ensemble (penalized feature selection in trees)	Recursive partitioning (if-then rules)
Công trình gốc	Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Tên gọi khác	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liên quan	5	5
Tóm tắt	Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.