GLM2Predict Example 1: Elastic Net for Gaussian Regression Prediction - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
8.00
1.0
Published
May 2019
Language
English (United States)
Last Update
2019-11-22
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blj1506016597986.ditamap
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B700-4003
lifecycle
previous
Product Category
Teradata Vantageā„¢

Input

SQL Call

SELECT * FROM GLM2Predict (
  ON glm2_elastic_net AS "input" PARTITION BY ANY
  ON glm2_elastic_net_model AS "model" DIMENSION ORDER BY "category"
  USING
  Accumulate ('id', 'employed')
) AS dt;

Output

For each input table row, the output has a predicted value and the value of lambda used for the prediction. The Lambda argument was not specified, so the function used the MinLambda value from the model table, glm2_elastic_net_model.

year employed lambda prediction
1947 60.323 0.00334451683575075 59.9414468498859
1948 61.122 0.00334451683575075 61.0021678204426
1949 60.171 0.00334451683575075 60.3241980362794
1950 61.187 0.00334451683575075 61.7107753923761
1951 63.221 0.00334451683575075 63.6156383593725
1952 63.639 0.00334451683575075 64.1341757230033
1953 64.989 0.00334451683575075 64.6267871265043
1954 63.761 0.00334451683575075 63.8946066816632
1955 66.019 0.00334451683575075 65.9912182654679
1956 67.857 0.00334451683575075 66.8166314439588
1957 68.169 0.00334451683575075 67.5400909226123
1958 66.513 0.00334451683575075 66.8174308174622
1959 68.655 0.00334451683575075 68.97512639576
1960 69.564 0.00334451683575075 69.4412744745363
1961 69.331 0.00334451683575075 69.3204175104195
1962 70.551 0.00334451683575075 70.920014180151
1947 60.323 0.00334451683575075 59.9414468498859
1948 61.122 0.00334451683575075 61.0021678204426
1949 60.171 0.00334451683575075 60.3241980362794