مقایسهٔ روش‌ها

	رگرسیون خطی (یادگیری ماشین)	درخت تصمیم
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	1805–1809	1984
پدیدآور	Legendre, A.-M. & Gauss, C.F.	Breiman, Friedman, Olshen & Stone
نوع	Supervised regression	Recursive partitioning (if-then rules)
منبع بنیادین	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., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
نام‌های دیگر	ordinary least squares regression, OLS, least squares regression, multiple linear regression	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
مرتبط	5	5
خلاصه	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.	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.