FILE: credit$\_$data.xlsX

Create a Regression Decision Tree to predict the Numeric target variable Age

Use the first $800$ records for the training data, and the last $200$ records for the evaluation data, as shown below:

trainingData credit$\_$data$[$c$(1$:$800),]$

evaluationData credit$\_$data$[$c$(801$:$1000),]$

Review the R Code Essential Steps for a Numeric $($Regression$)$ decision tree from question #$2$ above.

Modify the code as necessary to do the following:

$//$ Create the decision tree model from the credit$\_$data trainingData

$//$ Use the decision tree model to make predictions about the credit$\_$data evaluationData

$//$ Statistically evaluate how "close" the model's numeric predictions are to the actual credit$\_$data evaluationData

Use the accuracy$()$ function when you statistically evaluate how close the $200$ predicted Age values were to the $200$ actual Age

values. The Accuracy command will display a number of values as shown below. The RMSE value is NOT shown below.

MAE

MPE

MAPE

Test set $1\mathrm{.}20918$

$8\mathrm{.}926563-4\mathrm{.}48621,24\mathrm{.}56025$

What is the RMSE value?

Include $2$ decimal places in your answer.