Please help, thank you.

Which of the following is true regarding MSE {Mean Squared Error] 3.: MAE [Mean Absolute Error] in Linear Regression? C] There is a closed form solution to the optimal parameters when using MAE. C] The median minimizes the MSE tor a constant model. C] The MAE loss is not differentiable everywhere, which makes it impossible to take the gradient at those points. C] MAE encourages sparsity.r in the parameters [a lot of parameters are set to {II}, which allows for non-relevant features to not be included. C] If our data contains manyr corrupted outliers, MAE loss is a better metric than MSE loss. C] The optimal parameters found in a MSE loss function and a MAE loss function will never be equal. 05.2 if Points: 2 Which of the following models is linear in the parameters? C] fats} = ll-lads\" + 5x\" + E} + 93:: C] 3H3} = 3,211 31 'l' '5': C] lm} = '91 C] ME) = sflm + 92:: C] fs'lm} = that | 322:2 + Ilil3i2-.""*":m}I ass of Points: 3 Suppose I am given a dataset [-2,4,1,3,4] and I am using the constant model, y\":- = H. To find the optimal 3", I have the following two loss functions to work with. L Align H} = (By;- H): and L3[y,, H) = |2y,- 6|. Let 9:1, 3}} be the optimal parameters found for loss functions L A, L5- respectively. What is the relationship between 3:; and HE? E 3' 3:1 2::- HE. : j 3" = HE. : 3' 3:;