What are the advantages and disadvantages of using regularization methods like Ridge Regression?


  • Avoids overfitting a model.
  • They does not require unbiased estimators.
  • They add just enough bias to make the estimates reasonably reliable approximations to true population values.
  • They still perform well in cases of a large multivariate data with the number of predictors § larger than the number of observations (n).
  • The ridge estimator is preferably good at improving the least-squares estimate when there is multicollinearity.


  • They include all the predictors in the final model.
  • They are unable to perform feature selection.
  • They shrink the coefficients towards zero.
  • They trade the variance for bias.