CD Real Estate specializes in residential real estate services in the state of California. To complement the experience and local market knowledge of its licensed realtors, CD Real Estate wants to develop an analytical tool to predict the value of real estate. The file calireal contains data on some census tracts in California. The variables in these data are listed in Problem 20.

Predict the individuals’ credit scores using a k-nearest neighbors. Set aside 50% of the data as a test set and use 50% of the data for training and validation.

a. Based on all the input variables, determine the value of k that minimizes the RMSE in a validation procedure.

b. Experiment with different subsets of variables as input features and re-calibrate the value of k to minimize the RMSE. How does this k-nearest neighbors model compare to the model obtained in part (a)?

c. For the best-performing k-nearest neighbors model in the validation procedure, what is the RMSE on the test set?

Problem 20

CD Real Estate specializes in residential real estate services in the state of California. To complement the experience and local market knowledge of its licensed realtors, CD Real Estate wants to develop an analytical tool to predict the value of real estate. The file calireal contains data on some census tracts in California. The variables in these data are listed in the following table.

Predict the median house value using an individual regression tree. Set aside 50% of the data as a test set and use 50% of the data for training and validation.

Transcribed Image Text:

Variable Longitude Description Longitude in center of tract Latitude Age Latitude in center of tract Median age of housing Rooms Total number of rooms Bedrooms Total number of bedrooms Population Population of tract Households Number of households Income Value Median income Median house value of tract (in $1,000s)