Refer to the previous exercise for a description of the data set. Build a default regression tree to predict an NBA player’s salary (salary). Display the regression tree. 

a. What are the predictor variable and split value for the first split of the default regression tree? 

b. Build a full-grown tree. Which cp value is associated with the lowest cross-validation error? How many leaf nodes are in the minimum-error tree? 

c. Is there a simpler tree with a cross-validation error that is within one standard error of the minimum error? If there is, then which cp value is associated with the best-pruned tree? 

d. Prune the full tree to the best-pruned tree or the minimum error tree if the answer to part c is “No.” Display the tree. What are the rules that can be derived from the pruned tree? 

e. What are the ME, RMSE, MAE, MPE, and MAPE of the pruned tree on the validation data? 

f. Score the three NBA players Merrick is trying to sign as ACE Sports Management clients in the NBA_Score worksheet using the pruned tree. What is the average predicted salary of the three players?

Data from Exercises 42

Merrick Stevens is a sports analyst working for ACE Sports Management, a sports agency that represents over 200 athletes. He is interested in understanding the relationship between an NBA player’s salary and his physicality and performance statistics. Merrick has constructed a data set that contains information on 30 competing NBA teams and 445 players. A portion of the NBA_Data worksheet is shown in the table below. A detailed description of each field in the data set can be found in NBA Information.docx. Create a regression tree model for predicting an NBA player’s salary (salary). Select the best-pruned tree for scoring and display the full-grown, best-pruned, and minimum error trees.

Transcribed Image Text:

player_number 1 2 I 445 salary 947276 25000000 525093 age 36 37 1 23 *** points 13.5 17.6 I 1.2