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Decision Tree

note

Generated by ChatGPT, leaving it here for my own reference

Let's consider a simple decision tree used by a bank to decide whether to approve or decline a loan application.

Decision Criteria​

  • Credit Score: High (700 and above) or Low (below 700)
  • Employment Status: Employed or Unemployed
  • Annual Income: High (over $50,000) or Low (under $50,000)

Decision Tree Structure​

  • Root Node (First Decision Point): Credit Score
    • High Credit Score
    • Low Credit Score
  • Second Decision Point (for both High and Low Credit Score): Employment Status
    • Employed
    • Unemployed
  • Third Decision Point (for those who are Employed): Annual Income
    • High Income
    • Low Income
  • Leaf Nodes (Final Decision):
    • Approve Loan
    • Decline Loan

How the Decision Tree Works​

  • A loan application starts at the root (Credit Score).
  • If the Credit Score is high, it moves to the next decision point (Employment Status)
  • If the applicant is employed, the tree checks the Annual Income.
  • If the income is high, the loan is approved; if low, the loan might still be approved or declined based on other criteria (not shown in this basic tree).
  • If the Credit Score is low, the loan might be declined immediately, or it might move to the next decision point (Employment Status), where being unemployed would likely lead to a decline.
                     [Credit Score]
                    /             \
           [High Score]       [Low Score]
              /  \                 /    \
       [Employed] [Unemployed] [Employed] [Unemployed]
         /     \                     | 
[High Income] [Low Income]       [Decline Loan]
    |              | 
[Approve Loan] [Approve/Decline based on other criteria]