Decision Trees
Jeff and Maria approaching a bank to get a loan for their needs. Loan officer asked two questions.
1) Are you married?
They said yes... Great. A positive sign.
2) Are you both working?
They said yes. Officer checked their records and seems to be true and they are working in a company for long 3 years. Job stability is another positive sign.
Loan office then check their credit history and seems like they missed payment 3 times..A big negative.
Then he started thinking. Do I need to give them loan? Here comes the decision tree that helps to solve this issue.
Jeff and Maria approaching a bank to get a loan for their needs. Loan officer asked two questions.
1) Are you married?
They said yes... Great. A positive sign.
2) Are you both working?
They said yes. Officer checked their records and seems to be true and they are working in a company for long 3 years. Job stability is another positive sign.
Loan office then check their credit history and seems like they missed payment 3 times..A big negative.
Then he started thinking. Do I need to give them loan? Here comes the decision tree that helps to solve this issue.
- Classification approach that uses input variables to predict a classification variable.
- Builds one tree for each predictable attribute
- Do not support aggregation
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