Which statement best describes the function of the tuning parameter in lasso regression?

The tuning parameter in lasso regression plays a crucial role in managing the balance between model fit and complexity. Specifically, it shrinks the coefficients of less important variables towards zero. This mechanism encourages variable selection by effectively excluding variables that do not contribute significantly to the predictive power of the model. As the value of this parameter increases, the penalty for including additional variables becomes stronger, leading to a simpler model that retains only the most relevant predictors.

This process helps to prevent overfitting by limiting the number of variables included in the final model, fostering a more interpretable and robust model structure. By manipulating the tuning parameter, practitioners can optimize the model’s performance on unseen data while managing the trade-off between bias and variance.