True/False) Under the Probabilistic formulation, L2 (Ridge) regularization imposes Gaussian prior on the coefficients, while L1 (Lasso) regularization imposes Laplacian prior.1 pointTrueFalse

What concept/s under Probabilistic View is/are True? 1 pointWe can derive the posterior probability by knowing the probability of target and the prior distribution. The prior distribution is derived from independent draws of a prior coefficient density function that we choose when regularizing. L2 (ridge) regularization imposes a Gaussian prior on the coefficients, while L1 (lasso) regularization imposes a Laplacian prior. All of the above

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.

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

Whixh of the below statements are correct?1 pointNeither RidgeCV nor LassoCV use L1 regularization function.Both RidgeCV and LassoCV use L1 regularization function.Only RidgeCV use L1 regularization function.Only LassoCV use L1 regularization function.

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