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@@ -259,7 +259,7 @@ Thus, if we cannot assume that the expected outputs/targets are zero
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when all predictors are zero (the columns in the design matrix), it
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may be a bad idea to implement a model which penalizes the intercept.
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Furthermore, in for example Ridge and Lasso regression, the solutions
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(when not shrinking $$\beta_0$$) for the unknown parameters
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(when not shrinking $\beta_0$) for the unknown parameters
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$\bm{\beta}$ are derived under the assumption that both $\bm{y}$ and
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$\bm{X}$ are zero centered, that is we subtract the mean values.
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