विधियों की तुलना करें

	सपोर्ट वेक्टर मशीन (वर्गीकरण)	रैंडम फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1995	2001
प्रवर्तक	Cortes, C. & Vapnik, V.	Breiman, L.
प्रकार	Maximum-margin classifier (kernel method)	Ensemble (bagging of decision trees)
मौलिक स्रोत	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
उपनाम	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
संबंधित	5	4
सारांश	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.	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.