Follow the steps given in Machine Learning With R, Chapter 5, section “Example – Identifying Risky Bank Loans Using C5.0 Decision Trees.”
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UCI Machine Learning Data Repository (http://archive.ics.uci.edu/ml) by Hans Hofmann of the University of Hamburg. The dataset contains information on loans obtained from a credit agency in Germany.****
Prepare a brief that reports the execution steps and outcomes of the C5.0 lab. In addition, the following questions will be addressed in the brief:
Change the percentages of training and testing data to 60% and 40%, respectively. What is the effect for accuracy of the model? Can you explain the outcome?
Use the caret package to automatically tune the trials parameter. Were you able to identify a trials parameter that increased the accuracy from previous attempts? Show your work and the final result.
Specifically, the following critical elements must be addressed:
Description of dataset in use
Description of purpose of R commands used
Demonstration of R command execution.
Guidelines for Submission: Each report submission should be one to two pages in length. The report should briefly describe the dataset in use and the purpose
of the R commands used. The report should also include screenshots demonstrating successful completion of R command steps listed for each lab assignment.
Reports should follow these formatting guidelines: double-spacing, 12-point Times New Roman font, one-inch margins, and citations, if any, in APA format when
appropriate.
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