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...-linear. In this article, we will provide an overview of the least squares regression method, discuss its mathematical basis, and present its implementation in m ===Ordinary Least Squares===

...nderlying principles of ridge regularization, its implementation in linear regression, and its advantages and disadvantages. ==Ridge Regularization in Linear Regression==

...he model's complexity. L2 regularization is particularly useful for linear regression models, but can also be applied to other types of models. 2. '''Stability:''' Ridge regression is more stable than ordinary linear regression, especially when the features are multicollinear or the input matrix is ill

| '''[[BRR]]''' || || [[Bayesian ridge regression]] | '''[[CART]]''' || || [[Classification And Regression Tree]]