Which regression technique forces the coefficients of the last relevant feature to zero using the l1 norm?

Lasso regression is a technique designed to enhance the predictive accuracy and interpretability of the statistical model it produces by imposing an L1 penalty on the size of the coefficients. The L1 norm causes some coefficients to be exactly zero, effectively selecting a simpler model that excludes those features from the prediction. This characteristic makes Lasso particularly useful in situations where there are many predictors and it is desirable to identify a smaller number of them that contribute the most to the prediction. As a result, Lasso regression not only helps in regularizing the model to avoid overfitting but also performs feature selection, which can lead to a more interpretable model.

In contrast, Ridge regression, while also a regularization technique, applies an L2 penalty that shrinks coefficients but does not force them to be exactly zero. Elastic net regression combines both L1 and L2 penalties, allowing for a balance between feature selection and coefficient shrinkage, but it is not exclusively focused on driving coefficients to zero like Lasso. Polynomial regression, on the other hand, is not a regularization method but rather a type of regression that includes polynomial terms of the predictors to model nonlinear relationships. Therefore, Lasso regression clearly distinguishes itself with its ability to force the coefficients of less important features