Solved

To Examine the Local Housing Market in a Particular Region

Question 34

Essay

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:
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, 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, 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?
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?

Correct Answer:

verifed

Verified

a. A value of k = 2 minimizes the RMSE o...

View Answer

Unlock this answer now
Get Access to more Verified Answers free of charge

Related Questions

Q8: Test set is the data set used

Q25: _ is a generalization of linear regression

Q29: Which of the following is true of

Q30: k-means clustering is the process of:<br>A) agglomerating

Q30: _ is a measure of calculating dissimilarity

Q31: Average linkage is a measure of calculating

Q33: A bank is interested in identifying different

Q37: To examine the local housing market in

Q38: The endpoint of a k-means clustering algorithm

Q44: A cluster's _ can be measured by