Week 3: Sep 9 – Sep 13
Weekly Objectives
- Correlated variables and motivation of Ridge regression
- How to solve Ridge regression
- The source of bias and variance, and their trade-offs
- The effect of penalty and different ways of tuning it
- Connection between Ridge regression and PCA
- \(k\)-fold Cross-validation
- Scaling issue and other thing that could affect the performance
- Using the
glmnet
package and lm.ridge()
from the MASS
package
Lecture Notes and R Examples
Additional Readings
Homework