a. Explain the concept of the L1 and L2 regularization terms regularization ferms alfect the model's coefficients? b. Both Lasso and Rigge regression involve 384 the regularization hyperparameters (alpha or lambing penalty terms to the linear regression cost function. How do you choose the optimal values of models' bias-variance trade-off? Explain. c. In terms of feature selection, how can Lasso regression assist in identifying and eliminating irrelevant or redundant predictors? Describe the process and advantages of using Lasso for feature selection