পদ্ধতির তুলনা করুন

	রৈখিক নির্ভরণ (ML)	Random Forest
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1805–1809	2001
প্রবর্তক	Legendre, A.-M. & Gauss, C.F.	Breiman, L.
ধরন	Supervised regression	Ensemble (bagging of decision trees)
মৌলিক উৎস	Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
অপর নাম	ordinary least squares regression, OLS, least squares regression, multiple linear regression	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
সম্পর্কিত	5	4
সারসংক্ষেপ	Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.	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.