How does the regularization technique aid in addressing the Multicollinearity issue?
- By constraining the coefficient estimates, potentially setting some to zero.
- By increasing model complexity.
- By increasing the variance of the model.
- By reducing model bias.
Regularization techniques, such as Ridge and Lasso regression, can help address multicollinearity by adding a penalty term to the loss function that constrains the coefficients. In particular, Lasso regression can set some coefficients to zero, effectively performing feature selection.
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