GLML1L2 Arguments - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product

Teradata Vantage

Release Number

8.00

1.0

Published

May 2019

Language

English (United States)

Last Update

2019-11-22

dita:mapPath

blj1506016597986.ditamap

dita:ditavalPath

blj1506016597986.ditaval

dita:id

B700-4003

lifecycle

Product Category

Teradata Vantage™

FactorTable

[Optional] Specify the name for the output table that contains the result. The result is based on either CategoricalColumns or Randomization; therefore, you must also specify either 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 or randomization.

FeatureColumns

Specify the names of the InputTable columns that contain the variables to use as predictors (independent variables) in the model.

CategoricalColumns

[Optional] Specify the names of the InputTable columns that contain categorical variables, and which of their categories to use in the model.

categorical_column_and_categories	Descriptions
'categorical_column:max_cardinality'	Uses most common categories in categorical_column and groups other categories into category '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'.
'categorical_column:(category [,...])'	Uses specified categories of categorical_column and groups other categories into category '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'.
'categorical_column'	Uses all categories in categorical_column.

If you use this argument, you must also specify the FactorTable argument, and in the FeatureColumns argument, you must specify each categorical_column.

Default behavior: The function treats all variables as numerical.

For information about columns that you must identify as categorical, see Identification of Categorical Columns.

Randomization

[Optional] Specify whether to randomize the InputTable data. If you use this argument, you must also specify the FactorTable argument.

Default: 'false'

ResponseColumn

Specify the name of the InputTable column that contains the responses.

Family

[Optional] Specify the distribution exponential family.

Default: 'GAUSSIAN'

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 Functions.

alpha	Regularization Type	Parameter Description
0	Ridge	½
(0,1)	Elastic net
1	LASSO

Default: 0

Lambda

[Optional] Specify the parameter that controls the magnitude of the regularization term. A value of zero 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