The goal of this tutorial is to provide participants with a deep understanding of four widely used algorithms in machine learning:Generalized Linear Model (GLM), Gradient Boosting Machine (GBM), Random Forest and Deep Neural Nets. This includes a deep dive into the algorithms in the abstract sense, and a review of the implementations of these algorithms available within the R ecosystem.
Due to their popularity, each of these algorithms have several implementations available in R. Each package author takes a unique approach to implementing the algorithm, and each package provides an overlapping, but not identical, set of model parameters available to the user. The tutorial will provide an in-depth analysis of how each of these algorithms were implemented in a handful of R packages for each algorithm.
After completing this tutorial, participants will have a understanding of how each of these algorithms work, and knowledge of the available R implementations and how they differ. The participants will understand, for example, why the xgboost package has, in less than a year, become one of the most popular GBM packages in R, even though the gbm R package has been around for years and has been widely used -- what are the implementation tricks used in xgboost that are not (yet) used in the gbm package? Or, why do some practioners in certain domains prefer the one implementation over another? We will answer these questions and more!
For details, refer to tutorial description.
