Mastering Machine Learning with R - Second Edition

Summary

In this chapter, the goal was to use a small dataset to provide an introduction to practically apply an advanced feature selection for linear models. The outcome for our data was quantitative, but the glmnet package we used also supports qualitative outcomes (binomial and multinomial classifications). An introduction to regularization and the three techniques that incorporate it were provided and utilized to build and compare models. Regularization is a powerful technique to improve computational efficiency and to possibly extract more meaningful features when compared to the other modeling techniques. Additionally, we started to use the caret package to optimize multiple parameters when training a model. Up to this point, we've been ...