This example uses logistic regression to predict which students are admitted to an academic program.
Input
- InputTable: admissions_train, as in GLM Example 1: Logistic Regression Analysis with Intercept
SQL Call
Because the example uses logistic regression, the Family is Binomial. The Alpha value, 0, specifies a Ridge model.
SELECT * FROM GLM2 ( ON admissions_train AS InputTable OUT TABLE ModelTable (glm2_ridge_model) OUT TABLE RegularizationTable (glm2_2regularization) USING InputColumns ('masters', 'gpa', 'stats', 'programming') CategoricalColumns ('masters', 'stats', 'programming') ResponseColumn ('admitted') Family ('binomial') Intercept ('TRUE') NumLambdas (10) Alpha(0) ) AS dt;
Output
dfDevRatio | devRatio | dfDev | deviance | lambda |
---|---|---|---|---|
0 | 0 | 39 | 51.7957311227706 | 216.083130280731 |
6 | 0.00164825659995313 | 33 | 51.7103584670981 | 77.6562506120565 |
6 | 0.00453438694628144 | 33 | 51.5608692356944 | 27.9082094529445 |
6 | 0.0122307765430854 | 33 | 51.1622291095222 | 10.0296904464313 |
6 | 0.0313506707268246 | 33 | 50.1719002112855 | 3.60448385701157 |
6 | 0.0714771723905868 | 33 | 48.0935187202118 | 1.29538433362914 |
6 | 0.132326934855416 | 33 | 44.9417607846991 | 0.465536991807485 |
6 | 0.191442862387798 | 33 | 41.8798080971586 | 0.167305320216424 |
6 | 0.228448387997473 | 33 | 39.9630798426231 | 0.0601264145820994 |
6 | 0.2437883725835 | 33 | 39.1685341255778 | 0.0216083130280731 |
df_dev_ratio | deviance_ratio | df_dev | deviance | lambda | intercept | gpa | masters_yes | programming_beginner | programming_novice | stats_beginner | stats_novice |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 39 | 51.7957311227706 | 216.083130280731 | 0.6190392084 | -0 | -0 | -0 | 0 | -0 | -0 |
6 | 0.00164825659995313 | 33 | 51.7103584670981 | 77.6562506120565 | 0.6244703657 | -0.0002657335 | -0.0055716531 | -0.0050407358 | 0.0013575168 | -0.000727557 | -0.0002425326 |
6 | 0.00453438694628144 | 33 | 51.5608692356944 | 27.9082094529445 | 0.6340226614 | -0.0007208063 | -0.0153981003 | -0.0139177768 | 0.0037043543 | -0.0019840785 | -0.0006762965 |
6 | 0.0122307765430854 | 33 | 51.1622291095222 | 10.0296904464313 | 0.6597683095 | -0.0018694488 | -0.0420539111 | -0.0379125023 | 0.0097655079 | -0.0052208953 | -0.001889708 |
6 | 0.0313506707268246 | 33 | 50.1719002112855 | 3.60448385701157 | 0.7256166026 | -0.0042964122 | -0.1113489309 | -0.0996760118 | 0.0234260316 | -0.0124548896 | -0.0052622636 |
6 | 0.0714771723905868 | 33 | 48.0935187202118 | 1.29538433362914 | 0.8750560357 | -0.0072555821 | -0.2739921211 | -0.240858846 | 0.043509692 | -0.0227188283 | -0.013970035 |
6 | 0.132326934855416 | 33 | 44.9417607846991 | 0.465536991807485 | 1.1529184558 | -0.0062498019 | -0.5862205516 | -0.4961953742 | 0.0348997352 | -0.016655652 | -0.0306068568 |
6 | 0.191442862387798 | 33 | 41.8798080971586 | 0.167305320216424 | 1.5819520541 | -0.0054173069 | -1.0345059039 | -0.8337267959 | -0.0842809189 | 0.0476614506 | -0.0454722578 |
6 | 0.228448387997473 | 33 | 39.9630798426231 | 0.0601264145820994 | 2.1411288741 | -0.0253104343 | -1.5024463472 | -1.1771181428 | -0.3400969929 | 0.1821811309 | -0.0393956514 |
6 | 0.2437883725835 | 33 | 39.1685341255778 | 0.0216083130280731 | 2.6727140074 | -0.0610158153 | -1.8601449007 | -1.4574162679 | -0.6238441031 | 0.3326284335 | -0.0126211852 |