Week 9: Oct 21 – Oct 25
Weekly Objectives
- Understand the basic structure of fitting trees and random
forests
- Splitting rules in both classification and regression settings
- Know how to tune
mtry
, nodesize
and other
parameters using the randomForest
package
- Know the effect of these different tuning on the performance,
especially the impact on the bias-variance trade-off.
- Understand the kernel view of random forests
- Understand the Adaboost algorithm
- Compare linear boosting with Lasso
- Compare boosting with random forests
Lecture Notes and R Examples
Additional Readings
Homework