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Data Science and Predictive Analytics (UMich HS650)

Assessment: 16. Variable/Feature Selection

1 Wrapper feature selection

• Explain the three major types of feature selection methods
  • Filter
  • Wrapper
  • Embedded

2 Feature Selection in Parkinson’s Disease (PPMI Data)

Use the 06_PPMI_ClassificationValidationData_Short dataset setting ResearchGroup as class variable.

• Delete irrelevant columns (e.g. X, FID_IID) and select only the PD and Control cases
• Properly convert the variables types
• Apply Boruta to train a model, try different parameters (e.g., try different pValue, maxRuns). What are the differences?
• Summarize and visualize the results
• Apply Random Feature Elimination (RFE) and tune the model size
• Evaluate the Boruta model performance by comparing with REF
• Report and compare the variables selected by both methods. How much overlap is there in the selected variables?

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