The LPASSO method for regression regularization
نویسندگان
چکیده
Linear models are often built to understand how a set of input data affects output data and predict its value. A linear regression model assumes that that regression function E(Y |X) is linear in the inputsX1, . . . ,Xp. The output is represented as a weighted sum of the input variables (or features), where the weights are called the regression coefficients. The regressions coefficients are ascertained by minimizing the mean square error between the output and the predicted output for a given data. This technique to find the regression coefficients is called Ordinary least Squares (OLS). OLS is regularized to make these linear models more accurate. In [7] and [3], a detailed treatment of the subject of regularization of OLS is given, whose salient points are discussed here. OLS regression is regularized for the following reasons:
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