Input
The input table is admission_train, as in GLM Example: Logistic Regression Analysis with Intercept.
SQL Call
Because the response variable is binary (the admitted column has two possible values), the call specifies Family ('BINOMIAL'). Alpha (0) indicates L2 (ridge regression) regularization.
DROP TABLE admissions_model; DROP TABLE admissions_factor_table; CREATE MULTISET TABLE admissions_model AS ( SELECT * FROM GLML1L2 ( ON admissions_train AS InputTable OUT TABLE FactorTable (admissions_factor_table) USING TargetColumns ('masters', 'gpa', 'stats', 'programming') CategoricalColumns ('masters', 'stats', 'programming') ResponseColumn ('admitted') Family ('BINOMIAL') Alpha (0) RegularizationLambda (0.02) ) AS dt ) WITH DATA;
Output
SELECT * FROM admissions_model;
attribute category estimate information -------------- -------- --------------------- ----------- AIC NULL 15.21927981934978 NULL programming beginner -1.0259430213730834 p Features # NULL 6.0 NULL programming novice -0.0820786516340258 p masters yes -1.2652530272653697 p Iterations # NULL 28.0 NULL Lambda NULL 0.02 NULL Alpha NULL 0.0 NULL stats beginner 0.08063465501463249 p Regularization NULL NULL Ridge stats novice -0.026716553307241597 p Family NULL NULL Binomial Converged NULL NULL true gpa NULL 0.38346423433872745 p Rows # NULL 40.0 NULL BIC NULL 27.041435998147332 NULL (Intercept) NULL 0.3838162407664626 p
select * from admissions_factor_table;
masters_yes stats_beginner stats_novice programming_beginner programming_novice gpa admitted ----------- -------------- ------------ -------------------- ------------------ ---- -------- 1 0 1 1 0 3.95 0.0 0 0 0 0 0 3.83 1.0 1 0 1 0 1 2.33 1.0 1 0 0 1 0 3.85 0.0 1 0 1 1 0 3.46 0.0 0 0 0 0 1 4.0 1.0 1 0 0 1 0 3.75 0.0 1 0 0 1 0 3.46 0.0 0 0 1 0 1 3.52 1.0 0 0 0 0 0 3.13 1.0 0 0 1 1 0 3.68 1.0 0 0 0 0 0 3.82 1.0 0 0 1 0 1 3.65 1.0 0 0 0 0 0 3.93 1.0 1 0 0 0 0 3.96 0.0 0 0 0 0 0 3.7 1.0 1 1 0 0 1 3.5 1.0 0 0 1 0 1 3.55 1.0 1 0 0 0 0 1.98 0.0 0 0 0 0 0 3.71 1.0 0 0 0 0 1 3.0 0.0 1 0 0 1 0 2.65 1.0 1 0 0 0 0 4.0 1.0 1 0 0 0 0 3.57 1.0 1 0 0 0 1 3.79 0.0 0 0 1 0 1 3.44 0.0 1 0 0 0 0 3.45 0.0 0 0 0 0 1 1.87 1.0 1 0 0 1 0 3.5 1.0 0 0 1 1 0 3.7 1.0 1 0 1 1 0 4.0 0.0 1 1 0 1 0 3.95 0.0 1 1 0 0 0 3.5 1.0 1 0 0 0 0 3.9 1.0 1 0 0 0 1 3.59 1.0 1 0 0 0 0 3.81 1.0 0 0 1 1 0 3.87 1.0 0 1 0 0 0 3.6 1.0 0 0 0 0 0 3.96 1.0 1 1 0 1 0 3.76 0.0
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