CS 445 Machine Learning
Fall 2020
Week 6 Study Guide
Learning Objectives
- Define and characterize generalization error in terms of bias, variance, and irreducible error
- Define how bagging can be used to reduce variance and improve generalization error
- Define boosting and Adaboost how it addresses hard to classify examples in the training data.
- Define and describe a random forest and list how they can be used to improve generalization error.
Resources
Deliverables
| Topic | Description |
| Bias Variance Lab | Complete the in-class assignment and submit to Canvas |
| Random Forest lab | Complete the in-class assignment and submit to Canvas |