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This document discusses several regularized regression methods: Ridge regression, LASSO regression, and LARS regression. Ridge regression minimizes the residual sum of squares subject to a constraint on the L2-norm of the coefficients. LASSO regression is similar but uses an L1-norm constraint. Both Ridge and LASSO regressions can be solved using a singular value decomposition of the design matrix. LARS regression generalizes these approaches.
- Author
- hungbkpro90
- Language
- EN