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When is Ridge regression favorable over Lasso regression?
by Diamond (53,882 points) | 33 views

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Lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective.
by Wooden (3,542 points)

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