5.4.5 - Overview of 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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This chapter applies only to an instance of Teradata Warehouse Miner operating on a Teradata database.

Model scoring in Teradata Warehouse Miner is performed entirely through generated SQL, executed in the database (although PMML based scoring generally requires that certain supplied User Defined Functions be installed beforehand). A scoring analysis is provided for every Teradata Warehouse Miner algorithm that produces a predictive model (thus excluding the Association Rules algorithm).

Scoring applies a predictive model to a data set that has the same columns as those used in building the model, with the exception that the scoring input table need not always include the predicted or dependent variable column for those models that utilize one. In fact, the dependent variable column is required only when model evaluation is requested in the Tree Scoring, Linear Scoring and Logistic Scoring analyses.