See attached files.

MAT303 Module Six Problem Set Report

Decision Trees

[Your Full Name]

[Your SNHU Email]

Southern New Hampshire University

Note: Replace the bracketed text on page one (the cover page) with your personal information.

1. Introduction

Discuss the statement of the problem with regard to the statistical analyses that are being performed. Address the following questions in your analysis:

· What is the data set that you are exploring?

· How might your results be used?

· What types of analyses will you be running in this problem set?

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.

2. Data Preparation

There are some important variables that you have been asked to analyze in this problem set. Identify and explain these variables. Address the following questions in your analysis:

· What are the important variables in this data set?

· How many rows and columns are present in this data set?

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.

3. Classification Decision Tree

Reporting Results

· Use set.seed(705526) and split the credit card default data set into training and validation sets using 70% and 30% split, respectively. How many rows are in the original data set, the training set, and the validation set?

· Use set.seed(705526) and create a classification decision tree for the default variable using missed payment, credit utilization, and assets as predictors. Include the cost-complexity (cp) table.

· Plot the validation error against the cost-complexity parameter (cp). What is an appropriate cp value to use in pruning the tree?

· Use set.seed(705526) and prune the tree using the appropriate cp value and include the plot of the resulting decision tree.

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.
Evaluating Utility of Model

Evaluate the utility of the classification decision tree. Address the following questions in your analysis:

· Obtain the confusion matrix and report the counts for true positives, true negatives, false positives, and false negatives.

· Report the following:

· Accuracy

· Precision

· Recall

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.
Making Predictions Using Model

Make predictions using the regression model. Address the following questions in your analysis:

· What is the prediction for defaulting on credit for an individual who has not missed payments, owns a car and a house, and has a 30% credit utilization?

· What is the prediction for defaulting on credit for an individual who has missed payments, does not have any assets, and has a 30% credit utilization?

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.

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. Regression Decision Tree

Reporting Results

· Use set.seed(705526) and split the economic data set into training and validation sets using 80% and 20% split, respectively. How many rows are in the original data set, the training set, and the validation set?

· Use set.seed(705526) and create a regression decision tree for wage growth using economy, unemployment, and gdp as predictors. Include the cost-complexity (cp) table.

· Plot the validation error against the cost-complexity parameter (cp). What is an appropriate cp value to use in pruning the tree?
· Use set.seed(705526) and prune the tree using the appropriate cp value and include the plot of the resulting decision tree.

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.
Evaluating Utility of Model

Evaluate the utility of the classification decision tree. Address the following question in your analysis:

· What is the root mean squared error for the regression decision tree? Interpret this value.

Answer the question in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.

Making Predictions Using Model

Make predictions using the regression model. Address the following questions in your analysis:

· What is the predicted wage growth if the economy is not in recession, unemployment is at 3.4%, and the GDP growth rate is 3.5%?

· What is the predicted wage growth if the economy is in recession, unemployment is at 7.4%, and the GDP growth rate is 1.5%?

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.

5. Conclusion

Describe the results of the statistical analyses and address the following questions:

· Fully describe what these results mean for your scenario using proper descriptions of statistical terms and concepts.

· What is the practical importance of the analyses that were performed?

Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R code in your report.

6. Citations

You are not required to use external resources for this report. If none were used, remove this entire section. However, if you used any resources to help you with your interpretation, you must cite them. Use proper APA format for citations.

Insert references here in the following format:

Author’s Last Name, First Initial. Middle Initial. (Year of Publication). Title of book: Subtitle of book, edition. Place of Publication: Publisher.

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Overview

In each module, you will be learning about different statistical functions in R. You will apply these functions to specific data sets, creating models that can be used to understand and solve real-world problems. You will gain practice creating a model, reporting and interpreting its statistics, evaluating its significance, and using it to make predictions.

Note: Begin working on the readings and the problem set early each week. This will help make sure that you are prepared for the weekly discussion.

Prompt

In this activity, you will explore classification and regression decision tree models that have been created for you. Then you will be asked to create your own decision trees, and write a mini-report based on your findings.

1. Access the R scripts for this problem set by using the Jupyter Notebook link in Module Six. In your Jupyter Notebook, you have been given a set of steps that explains how to create classification and regression decision trees. Go through each step, examining the scripts and their output. If you are not sure how a specific script works or how to understand the output of a script, review the readings. Reach out to your instructor if you need additional help.

2. Review the 

Module Six Problem Set Report template

 to understand the questions that you will need to answer for this assignment. Then, write your own scripts to create the decision trees described in your report. Refer to the scripts that you were given as examples to guide your work.

3. Use the outputs of your scripts to answer all of the questions in your problem set report. The report has been divided into several sections. Each section contains questions to guide your analysis. Be sure to fully answer all of the questions and complete the following sections:

· Introduction: Communicate all ideas by presenting the context of your analyses.

· Reporting Results: Report the results of the model applying training and testing sets and interpreting plots.

· Evaluating Utility of Model: Evaluate the utility of the model by using the confusion matrix and root mean squared error.

· Making Predictions Using the Model: Make predictions based on the model by reporting prediction values.

· Conclusion: Communicate all ideas by summarizing and interpreting the practical implications of the results.

Guidelines for Submission

You will submit your completed problem set report as a Word document. Use 11-point Calibri font and one-inch margins. You must use the equation editor where appropriate.

You will also submit the HTML file containing the outputs of your R scripts from the Jupyter Notebook.