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Very basic question here, but I would like to understand (not mathematically) how the fact to add a "penalty" (sum of squared coeff. times a scalar) to the residual sum of square can reduce big coefficients ? thanks !

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Because your "penalty" represenation of the minimization problem is just the langrange form of a constraint optimization problem:

Assume centered variables. In both cases, lasso and ridge, your unconstrained target function is then the usual sum of squared residuals; i.e. given $p$ regressors you minimize: $$RSS(\boldsymbol{\beta}) = \sum_{i=1}^n (y_i-(x_{i,1}\beta_1 +\dots +x_{i,p}\beta_p))^2.$$ over all $\boldsymbol{\beta} =(\beta_1,\dots, \beta_p)$.

Now, in the case of a ridge regression you minimize $RSS(\boldsymbol{\beta})$ such that $$\sum_{i=1}^p\beta_p^2 \leq t_{ridge},$$ for some value of $t_{ridge}\geq 0$. For small values of $t_{ridge}$ it will be impossible to derive the same solution as in standard least square scenario in which case you only minimize $RSS(\boldsymbol{\beta})$ -- Think about $t_{ridge}=0$ then the only possible solution can be $\beta_1\equiv \dots \equiv \beta_p = 0$.

On the other hand, in the case of the lasso, you minimize $RSS(\boldsymbol{\beta})$ under the constraint $$\sum_{i=1}^p|\beta_p| \leq t_{lasso},$$ for some value of $t_{lasso}\geq 0$.

Both constrained optimization problems can be equivalently forumlated in terms of an unconstrained optimization problem, i.e. for the lasso: you can equivalently minimize

$$\sum_{i=1}^n (y_i-(x_{i,1}\beta_1 +\dots +x_{i,p}\beta_p))^2 + \lambda_{lasso}\sum_{i=1}^p|\beta_p|.$$

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  • $\begingroup$ Thanks, i'll have to deep into the "constrained to unconstrained" part but i got the idea $\endgroup$ – TmSmth Jul 23 '18 at 9:57

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