**Comprehension – Ridge and Lasso Regression**

Ridge Regression is a technique for analysing multiple linear regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value. By adding a degree of bias to the regression estimates, ridge regression reduces the standard test errors.

Consider a data set (split into training and test) on which you build a ridge regression model. Assume that only the raw attributes have been used and no new features have been developed for model building.

Based on the information above, answer the following questions.