1.0 - 8.00 - GLM2 Example 1: LASSO for Poisson Regression Analysis - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.0
8.00
Release Date
May 2019
Content Type
Programming Reference
Publication ID
B700-4003-098K
Language
English (United States)

This example uses Poisson regression to predict the number of awards won by students, based on their programming levels and math scores.

Input

awards
id num_awards prog math
45 0 3 41
108 0 1 41
15 0 3 44
67 0 3 42
... ... ... ...
196 0 2 49
36 1 1 44
155 1 1 46
6 0 2 46
... ... ... ...
38 3 2 50
182 0 2 43
115 0 1 43
14 1 2 54
... ... ... ...

SQL Call

Because the response variable is a count value (number of awards), the Family is Poisson. The Alpha value, 1, specifies a LASSO model.

SELECT * FROM GLM2 (
  ON awards AS InputTable
  OUT TABLE ModelTable (glm2_lasso_model)
  OUT TABLE RegularizationTable (glm2_regularization)
  USING
  InputColumns ('prog', 'math')
  ResponseColumn ('num_awards')
  Family ('POISSON')
  Intercept ('TRUE')
  NumLambdas (10)
  Alpha (1)
) AS dt;

Output

Onscreen Output
dfDevRatio devRatio dfDev deviance lambda
0 2.3155999606359e-15 199 287.672234452864 0.522060074545705
1 0.252835382560055 198 214.938515003069 0.187618662922922
1 0.285724516586101 198 205.477224328577 0.0674266514397151
2 0.291092364886125 197 203.933043413905 0.024231882124864
2 0.292547804908229 197 203.514353730634 0.00870848690800394
2 0.292738550188869 197 203.459481609541 0.00312966792410483
2 0.29276365104296 197 203.452260790758 0.0011247443348819
2 0.29276688972071 197 203.451329113093 0.000404212155898542
2 0.292767310601548 197 203.451208037361 0.000145266316894411
2 0.292767359699846 197 203.451193913144 5.22060074545706e-05
glm2_lasso_model
category information value
A FAMILY : POISSON  
A RESPONSE : num_awards  
A REGULARIZER : LASSO  
A CONVERGED : true  
A NUMOBSERVATIONS 200
B has_intercept 1
B alpha 1
B devnull 287.672234452865
B maxNumLambdas 10
B maxIterNum 100000
B threshold 1e-07
B minLambdaRatio 0.0001
B iterationNum 79
B minLambda 5.22060074545706e-05
C intercept -5.5766613646
C math 0.0861044506
C prog 0.1230769507
glm2_regularization
df_dev_ratio deviance_ratio df_dev deviance lambda intercept math prog
0 2.3155999606359e-15 199 287.672234452864 0.522060074545705 -0.4620354596 0 0
1 0.252835382560055 198 214.938515003069 0.187618662922922 -3.474053956 0.0546564845 0
1 0.285724516586101 198 205.477224328577 0.0674266514397151 -4.6383122967 0.0745575487 0
2 0.291092364886125 197 203.933043413905 0.024231882124864 -5.1481338496 0.0819282453 0.0350038202
2 0.292547804908229 197 203.514353730634 0.00870848690800394 -5.4215383741 0.0846018544 0.0911403822
2 0.292738550188869 197 203.459481609541 0.00312966792410483 -5.5210823104 0.0855658029 0.1116709262
2 0.29276365104296 197 203.452260790758 0.0011247443348819 -5.5575725933 0.0859216969 0.1190959763
2 0.29276688972071 197 203.451329113093 0.000404212155898542 -5.5706478142 0.0860486438 0.1217705047
2 0.292767310601548 197 203.451208037361 0.000145266316894411 -5.5753457856 0.0860942091 0.1227324598
2 0.292767359699846 197 203.451193913144 5.22060074545706e-05 -5.5766613646 0.0861044506 0.1230769507