विधियों की तुलना करें

	रेखीय प्रतिगमन (ML)	निर्णय वृक्ष
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1805–1809	1984
प्रवर्तक	Legendre, A.-M. & Gauss, C.F.	Breiman, Friedman, Olshen & Stone
प्रकार	Supervised regression	Recursive partitioning (if-then rules)
मौलिक स्रोत	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.
उपनाम	ordinary least squares regression, OLS, least squares regression, multiple linear regression	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
संबंधित	5	5
सारांश	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.