Draw an example (of your own invention) of a partition of two-dimensional feature space that could result from recursive binary splitting. Your example should contain at least six regions. Draw a decision tree corresponding to this partition. Be sure to label all aspects of your figures, including regions \(R_1, R_2, ...\), the cut points \(t_1, t_2, ...\), and so forth.
Provide a detailed explanation of the algorithm that is used to fit a regression tree.
Explain the difference between bagging, boosting, and random forests.
You will be using the lending_club data found modeldata.
The response is Class and the remaining variables are predictors.
Do test-training split as usual, and fit a random forest model or boosted tree (your choice) and a linear regression model.
The random forest or boosted tree model has a selection of hyper-parameters that you can tune to improve performance. Perform hyperparameter tuning using k-fold cross-validation to find a model with good predictive power. How does this model compare to the linear regression model?