পদ্ধতির তুলনা করুন

	রেগুলারাইজড র্যান্ডম ফরেস্ট	নিয়ন্ত্রিত গ্রেডিয়েন্ট বুস্টিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2012	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
প্রবর্তক	Deng, H. & Runger, G.	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
ধরন	Regularized ensemble (penalized feature selection in trees)	Regularized ensemble (additive tree model)
মৌলিক উৎস	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
অপর নাম	RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
সম্পর্কিত	5	6
সারসংক্ষেপ	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.	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.