5.4.5 - Tutorial - Logistic 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 logistic regression 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 Logistic Regression Scoring analysis as follows:
    • Selected Table — twm_customer_analysis
    • Evaluate and Score — Enabled
    • Include Probability Score Column — Enabled
      • Column Name — Probability
    • Include Estimate from Threshold Column — Enabled
      • Column Name — Estimate
      • Threshold Default — 0.35
    • Prediction Success Table — Enabled
    • Multi-Threshold Success Table — Enabled
      • Threshold Begin — 0
      • Threshold End — 1
      • Threshold Increment — 0.05
    • Cumulative Lift Table — Enabled
    • Result Table Name — score_logistic_1
    • Index Columns — cust_id
  2. Run the analysis.
  3. Click Results when it completes.

    For this example, the Logistic Regression Scoring/Evaluation analysis generated the following pages. A single click on each page name populates Results with the item.

    Logistic Regression Model Scoring Report
    Resulting Scored Table <result_db>.score_logistic_1
    Number of Rows in Scored Table 747
    Prediction Success Table
      Estimate Response Estimate Non-Response Actual Total
    Actual Response 304.58 / 40.77% 70.42 / 9.43% 375.00 / 50.20%
    Actual Non-Response 70.41 / 9.43% 301.59 / 40.37% 372.00 / 49.80%
    Estimated Total 374.99 / 50.20% 372.01 / 49.80% 747.00 / 100.00%
    Multi-Threshold Success Table
    Threshold Probability Actual Response, Estimate Response Actual Response, Estimate Non-Response Actual Non-Response, Estimate Response Actual Non-Response, Estimate Non-Response
    0.0000 375 0 372 0
    0.0500 375 0 353 19
    0.1000 374 1 251 121
    0.1500 373 2 152 220
    0.2000 369 6 90 282
    0.2500 361 14 58 314
    0.3000 351 24 37 335
    0.3500 344 31 29 343
    0.4000 329 46 29 343
    0.4500 318 57 28 344
    0.5000 313 62 24 348
    0.5500 305 70 23 349
    0.6000 291 84 23 349
    0.6500 286 89 21 351
    0.7000 276 99 20 352
    0.7500 265 110 20 352
    0.8000 253 122 20 352
    0.8500 243 132 16 356
    0.9000 229 146 13 359
    0.9500 191 184 11 361
    Cumulative Lift Table
    Decile Count Response Response (%) Captured Response (%) Lift Cumulative Response Cumulative Response (%) Cumulative Captured Response (%) Cumulative Lift
    1 74.0000 73.0000 98.6486 19.4667 1.9651 73.0000 98.6486 19.4667 1.9651
    2 75.0000 69.0000 92.0000 18.4000 1.8326 142.0000 95.3020 37.8667 1.8984
    3 75.0000 71.0000 94.6667 18.9333 1.8858 213.0000 95.0893 56.8000 1.8942
    4 74.0000 65.0000 87.8378 17.3333 1.7497 278.0000 93.2886 74.1333 1.8583
    5 75.0000 66.0000 88.0000 17.6000 1.7530 344.0000 92.2252 91.7333 1.8371
    6 75.0000 24.0000 32.0000 6.4000 0.6374 368.0000 82.1429 98.1333 1.6363
    7 74.0000 4.0000 5.4054 1.0667 0.1077 372.0000 71.2644 99.2000 1.4196
    8 73.0000 2.0000 2.7397 0.5333 0.0546 374.0000 62.8571 99.7333 1.2521
    9 69.0000 1.0000 1.4493 0.2667 0.0289 375.0000 56.4759 100.0000 1.1250
    10 83.0000 0.0000 0.0000 0.0000 0.0000 375.0000 50.2008 100.0000 1.0000
    Data
    cust_id Probability Estimate
    1362480 1.00 1
    1362481 0.08 0
    1362484 1.00 1
    1362485 0.14 0
    1362486 0.66 1
    1362487 0.86 1
    1362488 0.07 0
    1362489 1.00 1
    1362492 0.29 0
    1362496 0.35 1
    ... ...