November 2019
Beginner
394 pages
10h 31m
English
Now that we have covered OLS, we will try to improve on that by using regularization and coefficient shrinkage using LASSO and Ridge regression. One of the problems with OLS is that occasionally, for some datasets, the coefficients assigned to the predictor variables can grow to be very large. Also, OLS can end up assigning non-zero weights to all predictors and the total number of predictors in the final predictive model can be a very large number. Regularization tries to address both problems, that is, the problem of too many predictors and the problem of predictors with very large coefficients. Too many predictors in the final model is disadvantageous because it leads to overfitting, ...