Chapter 4. Advanced Feature Selection in Linear Models

"I found that math got to be too abstract for my liking and computer science seemed concerned with little details -- trying to save a microsecond or a kilobyte in a computation. In statistics I found a subject that combined the beauty of both math and computer science, using them to solve real-world problems."

This was quoted by Rob Tibshirani, Professor, Stanford University at http://statweb.stanford.edu/~tibs/research_page.html.

So far, we examined the usage of linear models for both quantitative and qualitative outcomes with an emphasis on the techniques of feature selection, that is, the methods and techniques to exclude useless or unwanted predictor variables. We saw that the linear models can be quite effective in the machine learning problems. However, newer techniques that have been developed and refined in the last couple of decades or so can improve the predictive ability and interpretability above and beyond...