18 Oct A Gentle Introduction to Penalized Regression
1:00 pm - 2:30 pm
data science seminar | level: intermediate |
for questions related to this event, contact firstname.lastname@example.org
affiliation: Ghent University
High-dimensional data are large and complex data in which the number of predictive variables can be anywhere from a few dozen to many thousands. Business problems with large numbers of predictors include scoring credit risk, predicting retail customer behavior, exploring health care alternatives, and tracking the effect of new pharmaceuticals in the population.
Penalized regression methods are modern regression methods for analyzing high-dimensional data. They can be seen as alternatives to traditional selection methods such as forward, backward and stepwise selection for fitting linear or logistic regression models.
The seminar will give a gentle introduction to this type of methods with example R code. The Hitters dataset from the ISLR package will be used to demonstrate the ridge, lasso and elastic net penalization methods.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning: with applications in R. Corrected edition. New York: Springer.
Bob De Clercq