1 pointWhich of the following terms are added for regularization in RIDGERIDGE and LASSOLASSO regression, respectively?

Which of the following about Ridge Regularization is TRUE?1 pointIt enforces the coefficients to be lower, but not 0It minimizes irrelevant features It penalizes the size magnitude of the regression coefficients by adding a squared term All of the above

Question 5Which one of the 3 Regularization techniques: Ridge, Lasso, and Elastic Net, performs the fastest under the hood? 1 pointRidgeLassoElastic NetNone of the above

When working with regularization, what is the view that recalibrates our understanding of LASSO and a Ridge, as a base problem, where coefficients have particular prior distributions? 1 pointProbabilistic viewGeometric viewAnalytical viewRegression view

Which statement under the Probabilistic View is correct?1 pointRegularization imposes certain errors on the regression coefficients. Feedback: Incorrect! Please review the further Details of Regularization lessons. Regularization imposes certain priors on the regression coefficients. Regularization uses some regression coefficients to inflate the errors. Regularization coefficients do not take into consideration prior probabilities.

What is the primary difference between Lasso (L1) and Ridge (L2) regularization in terms of coefficient shrinkage?Review LaterLasso can reduce coefficients to zero, while Ridge cannot.Ridge can reduce coefficients to zero, while Lasso cannot.Both Lasso and Ridge can reduce coefficients to zero.Neither Lasso nor Ridge can reduce coefficients to zero.