Keynote Presentation |
Concurrent Sessions |
Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demos | Closing General Session with Refreshments
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demos | Closing General Session with Refreshments
| Thursday, February 15 | ||
| SC7 Statistical Learning Methods in R |
Thu, Feb 15, 1:30 PM - 5:30 PM
Salon E |
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| Instructor(s): Kelly Sue McConville, Swarthmore College | ||
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Applied statisticians are often confronted with difficult modeling problems where standard regression approaches are not appropriate. For example, it may be that the number of possible predictors is large relative to the sample size or that the relationship between the variables is non-linear. This course will cover several statistical learning techniques which are designed to handle these difficult modeling problems. In particular, we will study penalized regression techniques (lasso, ridge, elasticnet), non-parametric regression (regression and smoothing splines), and classification methods (support vector machines, trees). Using data from the Bureau of Labor Statistics, participants will learn how to fit these models in R. R Markdown files with the relevant code will be provided so that participants can actively follow along with the demonstrations.
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