What is alpha in ridge regression

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What is the parameter alpha in ridge regression and how does it influence the trained regression?
Mar 2 in Machine Learning by Dev
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‘L2 regularization‘ or Ridge Regularization  adds penalty equal to square of the magnitude of coefficients
L2 adds a factor of sum of squares of coefficients in the optimization objective. Thus, ridge regression optimizes the following:
Objective = Residual + α * (sum of square of coefficients)

Residual sum of square
(alpha) strikes a balance between minimizing Residual and minimizing the sum of squares of coefficients. may have a variety of values:
α=0 ; objective will be like linear regression

or α = ∞, coefficients will be zero
0 < α < ∞, coefficients will be between 0 and 1

Alpha would have an effect on the magnitude of the coefficients
answered Mar 2 by Nandini
• 5,480 points

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