In this example, a standard Gain Ratio tree was built to predict credit card ownership ccacct based on 20 numeric and categorical input variables. Notice that the tree initially built contained 100 nodes but was pruned back to only 11, counting the root node. This yielded not only a relatively simple tree structure, but also Model Accuracy of 95.72% on this training data.
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Parameterize a Decision Tree as follows:
- Available Tables — twm_customer_analysis
- Dependent Variable — ccacct
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Independent Variables
- income
- age
- years_with_bank
- nbr_children
- gender
- marial_status
- city_name
- state_code
- female
- single
- married
- separated
- ckacct
- svacct
- avg_ck_bal
- avg_sv_bal
- avg_ck_tran_amt
- avg_ck_tran_cnt
- avg_sv_tran_amt
- avg_sv_tran_cnt
- Tree Splitting — Gain Ratio
- Minimum Split Count — 2
- Maximum Nodes — 1000
- Maximum Depth — 10
- Bin Numeric Variables — Disabled
- Pruning Method — Gain Ratio
- Include Lift Table — Enabled
- Response Value — 1
- Run the analysis.
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Click Results when it completes.
For this example, the Decision Tree analysis generated the following pages. A single click on each page name populates the page with the item.
Decision Tree Report Total observations 747 Nodes before pruning 33 Nodes after pruning 11 Model Accuracy 95.72% Variables: Dependent Dependent Variable ccacct Variables: Independent Independent Variables income ckacct avg_sv_bal avg_sv_tran_cnt Confusion Matrix Actual Non-Response Actual Response Correct Incorrect Predicted 0 340 / 45.52% 0 / 0.00% 340 / 45.52% 0 / 0.00% Predicted 1 32 / 4.28% 375 / 50.20% 375 / 50.20% 32 / 4.28% Cumulative Lift Table Decile Count Response Response (%) Captured Response (%) Lift Cumulative Response Cumulative Response (%) Cumulative Captured Response (%) Cumulative Lift 1 5.00 5.00 100.00 1.33 1.99 5.00 100.00 1.33 1.99 2 0.00 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 3 0.00 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 4 0.00 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 5 0.00 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 6 402.00 370.00 92.04 98.67 1.83 375.00 92.14 100.00 1.84 7 0.00 0.00 0.00 0.00 0.00 375.00 92.14 100.00 1.84 8 0.00 0.00 0.00 0.00 0.00 375.00 92.14 100.00 1.84 9 0.00 0.00 0.00 0.00 0.00 375.00 92.14 100.00 1.84 10 340.00 0.00 0.00 0.00 0.00 375.00 50.20 100.00 1.00