Week 4: Sep 16 – Sep 20
Weekly Objectives
- The solution the Lasso penalty in the one-variable case as an
optimization problem
- The effect of the Lasso penalty with the constrained formulation in
a two-dimensional case
- The different properties of Ridge and Lasso and know when to use
which
- Understand the connection and difference between Lasso, step-wise
regression and stage-wise regression.
- Fit Lasso and Elastic-net using the
glmnet
package and
extract useful information
- Use penalized logistic regression models for classification
problems
- Understand and address some common practical issues such as missing
values, outliers, highly skewed and non-standard distribution
Lecture Notes and R Examples
Additional Readings
Homework