How Does Lasso Regression Handle Multicollinearity?
Multicollinearity, the presence of highly correlated predictor variables, can be problematic in traditional regression approaches, leading to unstable estimates of coefficients. Lasso regression addresses this issue by shrinking the coefficients of less important variables to zero, effectively reducing multicollinearity and enhancing the model’s stability and predictive power.
