5.4.5 - Tutorial - Tree Scoring - Teradata Warehouse Miner

Teradata Warehouse Miner User Guide - Volume 3Analytic Functions

Product
Teradata Warehouse Miner
Release Number
5.4.5
Published
February 2018
Language
English (United States)
Last Update
2018-05-04
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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.

  1. 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
    • Targeted Confidence(s) - For binary outcome only — Enabled
      • Targeted Value — 1
    • Result Table Name — twm_score_tree_1
    • Primary Index Columns — cust_id
  2. Run the analysis.
  3. 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