Predicting Used Car Prices (Bootstrap Forest and Boosted Trees). Return to the Toyota Corolla data, and refit the partition model. (Hint: Use the recall button in the partition dialog). This time, choose bootstrap forest from the dialog window. Use the default settings.

a. Compared to final reduced tree above, how does the bootstrap forest behave in terms of overall error rate on the test set? Save the prediction formula for this model to the data table.

b. Run the same model again, but this time choose boosted tree from the partition dialog. Use the default settings.

c. How does the boosted tree behave in terms of the error rate relative to the reduced model and the bootstrap forest. Save the prediction formula for this model to the data table.

d. To facilitate comparison of error rates for the different models, use the Model Comparison platform (under Analyze $>$ Modeling). To view fit statistics for the different models, put the validation column in the Group field in the Model Comparison dialog.

i. Which model performs best on the test set?

ii. Explain why this model might have the best performance over the other models you fit.