In this example, the same table is scored as was used to build the decision tree model, as a matter of convenience. Typically, this would not be done unless the contents of the table changed since the model was built.
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Parameterize a Decision Tree Scoring Analysis as follows:
- Selected Tables — twm_customer_analysis
- Scoring Method — Evaluate and Score
- Use the name of the dependent variable as the predicted value column name — Enabled
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Targeted Confidence(s) - For binary outcome only — Enabled
- Targeted Value — 1
- Result Table Name — twm_score_tree_1
- Primary Index Columns — cust_id
- Run the analysis.
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Click Results when it completes.
For this example, the Decision Tree Scoring analysis generated the following pages. A single click on each page name populates Results with the item.
Decision Tree Model Scoring Report Resulting Scored Table Name score_tree_1 Number of Rows in Scored File 747 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 5.00 100.00 1.33 1.99 5.00 100.00 1.33 1.99 2 0 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 3 0 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 4 0 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 5 0 0.00 0.00 0.00 0.00 5.00 100.00 1.33 1.99 6 402 370.00 92.04 98.67 1.83 375.00 92.14 100.00 1.84 7 0 0.00 0.00 0.00 0.00 375.00 92.14 100.00 1.84 8 0 0.00 0.00 0.00 0.00 375.00 92.14 100.00 1.84 9 0 0.00 0.00 0.00 0.00 375.00 92.14 100.00 1.84 10 340 0.00 0.00 0.00 0.00 375.00 50.20 100.00 1.00 Data cust_id cc_acct _tm_target 1362480 1 0.92 1362481 0 0 1362484 1 0.92 1362485 0 0 1362486 1 0.92 … … …