Exam 2 Study Guide
Jump to navigation
Jump to search
Exam 2 will consist of approximately 3 multiple choice questions on each topic listed below:
- Comparison of different regression learner performance characteristics: Trees, forests, KNN, linreg
- Comparison of learner types: Regression, Classification, RL
- Overfitting: Definition, how to identify, what might prevent it, what might cause it?
- Bootstrap aggregating.
- Boosting.
- Decision trees. Random versus information based construction. Advantages of one over the other.
- Reinforcement learning: How is it defined? Questions about State, Action, Transitions, Reward
- Q-Learning. The update equation, definition of Q
- Dyna-Q
- Things you should know because you did the projects. In sample versus out of sample. Istanbul problem, why did shuffling help?
- Options
