Use the file CreditScore.xlsx (located in Chapter 9 data files) to answer this q

Question

Use the file CreditScore.xlsx (located in Chapter 9 data files) to answer this question. A consumer advocacy agency, Equitable Ernest, is interested in providing a service in which an individual can estimate their own credit score (a continuous measure used by banks, insurance companies, and other businesses when granting loans, quoting premiums, and issuing credit). The file CreditScore contains data on an individual’s credit score and other variables. Create a standard partition of the data with all the tracked variables and 50% of observations in the training set, 30% in the validation set, and 20% in the test set. Predict the individuals’ credit scores using k-Nearest Neighbors with up to k = 20. Use CreditScore 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 Report for all three sets of data.

What value of k minimizes the RMSE on the validation data?

How does the RMSE on the test set compare to the RMSE on the validation set?

What is the average error on the test set? Analyze the distribution of the residual output in the KNNP_TestScore worksheet by constructing a histogram.

Explanation / Answer

This example compares the results of the tree ensemble methods with the Single Tree method. On the XLMiner ribbon, from the Data Mining tab, select Partition - Standard Partition to open the Standard Partition dialog, then select a cell on the Data_Partition worksheet.

On the XLMiner ribbon, from the Data Mining tab, select Predict - Regression Tree - Single Tree to open the Regression Tree - Step 1 of 3 dialog.

At Output Variable, select MEDV, then from the Selected Variables list, select the remaining variables (except CAT.MEDV).

[Regression Tree - Step 1 of 3 Dialog]

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