Σύγκριση μεθόδων

	Γραμμική Παλινδρόμηση (ML)	Ενίσχυση Κλίσης (Gradient Boosting)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1805–1809	2001
Δημιουργός	Legendre, A.-M. & Gauss, C.F.	Friedman, J. H.
Τύπος	Supervised regression	Ensemble (sequential boosting of decision trees)
Θεμελιώδης πηγή	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	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Εναλλακτικές ονομασίες	ordinary least squares regression, OLS, least squares regression, multiple linear regression	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Συναφείς	5	5
Σύνοψη	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.