Input
The input table is admission_train, as in GLM Example 1: 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 FeatureColumns ('masters', 'gpa', 'stats', 'programming') CategoricalColumns ('masters', 'stats', 'programming') ResponseColumn ('admitted') Family ('BINOMIAL') Alpha (0) Lambda (0.02) Randomization ('t') ) AS dt ) WITH DATA;
Output
attribute | category | estimate | information |
---|---|---|---|
(Intercept) | 0.383816240766463 | p | |
masters | yes | -1.26525302726537 | p |
stats | Beginner | 0.0806346550146325 | p |
stats | Novice | -0.0267165533072416 | p |
programming | Beginner | -1.02594302137308 | p |
programming | Novice | -0.0820786516340258 | p |
gpa | 0.383464234338727 | p | |
Family | Binomial | ||
Regularization | Ridge | ||
Alpha | 0 | ||
Lambda | 0.02 | ||
Iterations # | 28 | ||
Converged | true | ||
Rows # | 40 | ||
Features # | 6 | ||
AIC | 15.2192798193498 | ||
BIC | 27.0414359981473 |
masters_yes | stats_Beginner | stats_Novice | programming_Beginner | programming_Novice | gpa | admitted | td_randomized_id |
---|---|---|---|---|---|---|---|
1 | 1 | 0 | 1 | 0 | 3.95 | 0 | 1569741360 |
0 | 0 | 1 | 1 | 0 | 3.7 | 1 | 516548029 |
1 | 0 | 1 | 0 | 1 | 2.33 | 1 | 1182054491 |
0 | 1 | 0 | 0 | 0 | 3.6 | 1 | 251269761 |
0 | 0 | 0 | 0 | 0 | 3.13 | 1 | 715581077 |
0 | 0 | 1 | 0 | 1 | 3.65 | 1 | 542832677 |
1 | 0 | 0 | 0 | 0 | 3.45 | 0 | 1316484262 |
0 | 0 | 0 | 0 | 0 | 3.7 | 1 | 49567875 |
1 | 0 | 0 | 0 | 0 | 1.98 | 0 | 672024888 |
0 | 0 | 1 | 1 | 0 | 3.87 | 1 | 790221947 |
... | ... | ... | ... | ... | ... | ... | ... |