Regarding the tuning parameter λ in the lasso model-fitting procedure, which statement is true?

In the context of the lasso (Least Absolute Shrinkage and Selection Operator) model, the tuning parameter λ plays a crucial role in determining the strength of the regularization applied to the regression coefficients. When λ is increased, the penalty for including predictors in the model becomes stronger, which means the lasso will tend to shrink more coefficients towards zero. As a result, the model effectively eliminates some predictors entirely, leading to a simpler model that focuses on the most significant variables.

This characteristic of lasso regularization is particularly valuable in situations with a large number of predictors, as it aids in variable selection and helps mitigate issues of overfitting. Therefore, with a higher λ value, the model becomes more parsimonious by reducing the number of predictors included, which enhances interpretability and can lead to better performance on validation data.

The other statements do not accurately reflect the behavior of the lasso model with respect to increasing λ. Increasing λ typically leads to higher bias and lower variance for the predictions, contrary to what is stated in other options, and it certainly does have effects on model complexity by influencing the number of predictors retained.