GLML1L2 Syntax Elements - Teradata Vantage

Machine Learning Engine Analytic Function Reference
Product
Teradata Vantage
Release Number
9.02
9.01
2.0
1.3
Published
February 2022
Language
English (United States)
Last Update
2022-02-10
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B700-4003
lifecycle
previous
Product Category
Teradata Vantage™
FactorTable
[Optional] Specify the name for the FactorTable. The function encodes categorical predictors as integer values in the FactorTable and copies numeric predictors to the FactorTable unchanged.
If you specify FactorTable, you must either specify CategoricalColumns or Randomization ('true').
You can use factor_table as InputTable for future GLML1L2 function calls, thereby saving the function from repeating the categorical-to-numerical conversion.
TargetColumns
Specify the names of the InputTable columns that contain the variables to use as predictors (independent variables) in the model.
Every target_column is numerical unless you specify it with CategoricalColumns.
CategoricalColumns
[Optional] Specify columnname-value pairs, each of which contains the name of a categorical input column and the category values in that column that the function is to include in the model that it creates.
columnname_value_pair Description
'columnname:max_cardinality' Limits categories in column to max_cardinality to most common ones and groups others together as 'others'.

For example, 'column_a:3' specifies that for column_a, function uses 3 most common categories and sets category of rows that do not belong to those 3 categories to 'others'.
'columnname:(category [,...])' Limits categories in column to those that you specify and groups others together as 'others'.

For example, 'column_a : (red, yellow, blue)' specifies that for column_a, function uses categories red, yellow, and blue, and sets category of rows that do not belong to those categories to 'others'.
'columnname' All category values appear in model.
For information about columns that you must identify as numeric or categorical, see Identification of Numeric and Categorical Columns.
ResponseColumn
Specify the name of the InputTable column that contains the responses.
Family
[Optional] Specify the distribution exponential family:
Option Model-Training Type
'GAUSSIAN' (default) Regression
'BINOMIAL' Classification
Alpha
[Optional] Specify the mixing parameter for penalty computation (see the following table). The alpha must be in [0, 1]. If alpha is in (0,1), it represents α in the elastic net regularization formula in Generalized Linear Model (GLM) Functions (ML Engine).
alpha Regularization Type Parameter Description
0 Ridge Formula for ridge regularization, used by Machine Learning Engine function GLML1L2
(0,1) Elastic net Formula for elastic net regularization, used by Machine Learning Engine function GLML1L2
1 LASSO Formula for LASSO regularization, used by Machine Learning Engine function GLML1L2
Default: 0
RegularizationLambda
[Optional] Specify the parameter that controls the magnitude of the regularization term. The value lambda must be in the range [0, 100]. The value 0 disables regularization.
Default: 0
StopThreshold
[Optional] Specify the convergence threshold. The threshold must be a nonnegative DOUBLE PRECISION value.
Default: 1.0e-7
MaxIterNum
[Optional] Specify the maximum number of iterations over the data. The parameter max_iterations must be a positive INTEGER value in the range [1, 100000].
Default: 10000
Randomization
[Optional. Unnecessary if input is randomized.] Specify whether to randomize the input.
Default: 'false'