What is a characteristic of lasso regression?

Lasso regression is designed to perform both variable selection and regularization, which helps to enhance the prediction accuracy and interpretability of statistical models. One of its fundamental characteristics is its ability to shrink some coefficients to zero. This property makes it particularly useful for identifying and selecting only the most relevant variables for a given problem, thereby simplifying the model. When applying lasso regression, the inclusion of a penalty term drives some regression coefficients towards zero as the complexity of the model is reduced. Therefore, this characteristic highlights lasso regression's power in creating sparse models where less informative predictors are excluded, leading to better performance in many situations.

While the other options present different properties of regression techniques, they do not apply to lasso regression in the same way. For instance, lasso does not necessarily include all predictors in the final model—this is the opposite of what it does due to its selection feature. Similarly, while lasso can help in cases of multicollinearity, it is more specifically aimed at feature selection by shrinking coefficients. Finally, lasso regression can be applied to various sample sizes; it doesn't strictly require a large sample size to be effective. Hence, the option that highlights the ability of lasso regression to shrink coefficients to zero is the defining