Which regression technique can handle the multicollinearity problem?

Ridge Regression and Lasso Regression are both techniques specifically designed to address the issue of multicollinearity, which occurs when independent variables in a regression model are highly correlated. This can lead to unstable estimates of the regression coefficients, making it difficult to ascertain the relationship between the predictors and the outcome variable.

Ridge Regression mitigates multicollinearity by adding a penalty term to the loss function that is proportional to the square of the coefficients. This penalty discourages large coefficients and distributes the effect among correlated predictors, ultimately leading to more stable estimates.

Lasso Regression also incorporates a penalty term, but it uses the absolute values of the coefficients. This can not only reduce the impact of multicollinearity but also performs variable selection, meaning it can shrink some coefficients entirely to zero, effectively removing some variables from the model.

Both techniques therefore enhance the robustness of regression analysis when facing multicollinearity challenges, making them superior to Ordinary Least Squares Regression in these circumstances. Ordinary Least Squares does not have mechanisms to handle multicollinearity, which can lead to inflated standard errors and unreliable confidence intervals for the coefficient estimates.

Thus, the correct approach to handle multicollinearity effectively is through the use of Ridge Regression and Lasso Regression, validating