Select one of the following two options:
Implement the gradient-boosting tree algorithm (with the usual fit-predict interface) as described in Sections 10.9-10.10 of Elements of Statistical Learning (2nd Edition). Answer the questions below as you did for Project 1.
Put your README below. Answer the following questions.
- What does the model you have implemented do and when should it be used?
- How did you test your model to determine if it is working reasonably correctly?
- What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.)
- Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental?
Implement generic k-fold cross-validation and bootstrapping model selection methods.
In your README, answer the following questions:
- Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)?
- In what cases might the methods you've written fail or give incorrect or undesirable results?
- What could you implement given more time to mitigate these cases or help users of your methods?
- What parameters have you exposed to your users in order to use your model selectors.
See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2.
As usual, above-and-beyond efforts will be considered for bonus points.