Refer to the previous exercise for a description of the data set. Create a regression tree model

Question:

Refer to the previous exercise for a description of the data set. Create a regression tree model for predicting per capita electricity retail sales (Sales). Select the best-pruned tree for scoring and display the full-grown, best-pruned, and minimum error trees.

a. How many leaf nodes are in the best-pruned tree and minimum error tree?

b. What are the predictor variable and split value for the first split of the best-pruned tree? What are the rules that can be derived from the root node?

c. What are the RMSE and MAD of the best-pruned tree on the test data?

d. What is the predicted per capita electricity retail sales for a state with the following values: Price = 11, Generation = 25, and Income = 65,000?

Data from Exercises 40

Kyle Robson, an energy researcher for the U.S. Energy Information Administration, is trying to build a model for predicting annual electricity retail sales for states. Kyle has compiled a data set for the 50 states and the District of Columbia that contains average electricity retail price (Price in cents/kWh), per capita electricity generation (Generation), median household income (Income), and per capita electricity retail sales (Price in MWh). A portion of the data set is shown in the accompanying table. Build a default regression tree to predict per capita electricity retail sales (Sales). Display the regression tree.