পদ্ধতির তুলনা করুন

	Random Forest	সাপোর্ট ভেক্টর মেশিন (শ্রেণীকরণ)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001	1995
প্রবর্তক	Breiman, L.	Cortes, C. & Vapnik, V.
ধরন	Ensemble (bagging of decision trees)	Maximum-margin classifier (kernel method)
মৌলিক উৎস	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
অপর নাম	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.