Which statement about lasso regression is true relative to ordinary least squares?

Lasso regression involves a regularization technique that adds a penalty for the absolute size of the coefficients. This allows it to perform variable selection and shrinkage concurrently, which means it can limit the influence of less important predictors and potentially lead to better prediction accuracy, especially in high-dimensional datasets.

The assertion that lasso regression is less flexible compared to ordinary least squares (OLS) is true because OLS does not impose any constraints on coefficient sizes, allowing them to potentially fit the model very closely to the data, which can lead to overfitting. This suggests it is more flexible or adaptable when fitting individual data points. However, this flexibility can cause issues in generalizability.

In the context of improving prediction accuracy, lasso may lead to a decrease in variance (because it limits the effect of some predictors) which can be advantageous when the boost in bias from the penalties doesn't lead to a significant overall increase in error. The decrease in variance must be greater than any potential increase in bias for the overall outcome to be an improvement.

Hence, choosing lasso regression can lead to better prediction accuracy when we have a situation with many predictors, as it effectively balances the bias-variance trade-off. This balance is crucial because a decrease in variance while slightly increasing