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

==Weighted Alternating Least Squares (WALS)== Weighted Alternating Least Squares (WALS) is a widely-used optimization algorithm employed in the field of [[m

...Logistic Regression]] and [[Support Vector Machines]] with linear kernels. Linear decision boundaries are computationally efficient and can provide accurate ...ear decision boundaries can effectively handle more complex datasets where linear boundaries are insufficient.

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

...regularization techniques. It is particularly relevant in the context of [[linear regression]] and [[logistic regression]] models, where regularization is em ...es adding a penalty term to the objective function based on the sum of the squares of the model's coefficients. Like L1 regularization, this penalty term is m

*[[generalized linear model]] *[[least squares regression]]

* '''Recursive least squares (RLS)''': A method for online linear regression that updates model parameters as new data is received, particula

| '''[[ELU]]''' || || [[Exponential Linear Unit]] | '''[[GLM]]''' || || [[Generalized Linear Model]]