To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below:

Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price using k-nearest neighbors with up to k = 10. Use Sale Price as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data.
a. What value of k minimizes the root mean squared error (RMSE) on the validation data?
b. What is the RMSE on the validation data and test data?
c. What is the average error on the validation data and test data? What does this suggest?

Question

To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below:

Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price using k-nearest neighbors with up to k = 10. Use Sale Price as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data. 
a. What value of k minimizes the root mean squared error (RMSE) on the validation data?
b. What is the RMSE on the validation data and test data?
c. What is the average error on the validation data and test data? What does this suggest?

Quizplus · Accepted Answer

The Answer of To examine the local housing market in a particular region, a sample of 120 homes sold during a year are collected. The data are given below:

Partition the data into training (50 percent), validation (30 percent), and test (20 percent) sets. Predict the sale price using k-nearest neighbors with up to k = 10. Use Sale Price as the output variable and all the other variables as input variables. In Step 2 of XLMiner's k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Generate a Detailed Scoring report for all three sets of data. 
a. What value of k minimizes the root mean squared error (RMSE) on the validation data?
b. What is the RMSE on the validation data and test data?
c. What is the average error on the validation data and test data? What does this suggest?

To Examine the Local Housing Market in a Particular Region