For this dataset, you are required to fit a decision tree classifier. Credit Score Income Employment
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For this dataset, you are required to fit a decision tree classifier.
Credit Score | Income | Employment Status | Years at Current Address | Loan Amount | Loan Approval |
---|---|---|---|---|---|
720 | $60,000 | Employed | 5 | $10,000 | Approved |
650 | $40,000 | Unemployed | 2 | $5,000 | Denied |
800 | $90,000 | Employed | 10 | $20,000 | Approved |
600 | $30,000 | Self-Employed | 3 | $8,000 | Denied |
700 | $80,000 | Employed | 7 | $15,000 | Approved |
750 | $70,000 | Employed | 8 | $12,000 | Approved |
670 | $45,000 | Unemployed | 1 | $4,000 | Denied |
680 | $55,000 | Self-Employed | 4 | $7,000 | Denied |
730 | $65,000 | Employed | 6 | $9,000 | Approved |
620 | $35,000 | Self-Employed | 2 | $6,000 | Denied |
i) There are 5 independent variables, some of which are continuous variables, how to you split their continuous variables in nodes? Use the first column "Credit Score" as an example to show your algorithm and calculation.
ii) for the first splitting node, which column should be split first? Use calculation to show why
Related Book For
Income Tax Fundamentals 2013
ISBN: 9781285586618
31st Edition
Authors: Gerald E. Whittenburg, Martha Altus Buller, Steven L Gill
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