Applied Predictive Modeling in R
At useR! 2014 I'll be doing a morning tutorial (09:15 – 12:45) on at Monday, 30 June.
This course will provide an overview of using R for supervised learning. The session will step through the process of building, visualizing, testing and comparing models that are focused on prediction. The goal of the course is to provide a thorough workflow in R that can be used with many different modeling techniques. A case study is used to illustrate functionality. Basic familiarity with R is required.
- Introduction (philosophy, case study)
- General strategies (data splitting, resampling, model tuning)
- Data pre-processing (transformations, variable filtering)
- Conventions in R (function interfaces, consistency)
- Building and tuning models (performance metrics, trees, kernel methods)
- Other Topics (as time allows) (comparing models via resampling, parallel processing)
I will be giving away about a dozen copies of the book (via names in a hat). Originally the book was to be included but since the tutorial is free, that isn't possible.
Note to Windows users: The last two classes that I have taught where Windows users installed packages via RStudio, all the dependencies have not been installed. I recommend installing packages using base R.
install.packages(c("caret", "pROC", "rpart", "partykit", "C50", "kernlab", "AppliedPredictiveModeling", "earth", "mda", "nnet"), dependencies = c("Depends", "Imports", "Suggests"))
You can test the install by loading