TD_GLM Output - Analytics Database

Database Analytic Functions

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Analytics Database
Release Number
17.20
Published
June 2022
Language
English (United States)
Last Update
2024-04-06
dita:mapPath
gjn1627595495337.ditamap
dita:ditavalPath
ayr1485454803741.ditaval
dita:id
jmh1512506877710
Product Category
Teradata Vantageā„¢
TD_GLM produces the following outputs:
  • Model (Primary output): Contains the trained model with model statistics. The following model statistics are stored in the model:
    • Loss Function
    • MSE (Gaussian)
    • Loglikelihood (Logistic)
    • Number of Observations
    • AIC
    • BIC
    • Number of Iterations
    • Regularization
    • Alpha (L1/L2/Elasticnet)
    • Learning Rate (initial)
    • Learning Rate (Final)
    • Momentum
    • Nesterov
    • LocalSGD Iterations (Partition by Any only)
  • [Optional for PARTITION BY ANY] MetaInformationTable (Secondary Output): Contains training progress information for each iteration. When the StepwiseDirection parameter is specified, the secondary output table contains information for the Stepwise Regression algorithm.
The model output schema for partition by any is as follows:
Output Schema for PARTITION BY ANY
Column Data Type Description
attribute SMALLINT Numeric index of predictor and model metrics. Intercept is specified using index 0, and the rest of the predictors take positive values. Model metrics take negative indices.
predictor VARCHAR Name of the predictor or model metric.
estimate FLOAT Predictor weights and numeric-based metric values.
value VARCHAR String-based metric values such as SQUARED_ERROR for LossFunction, L2 for Regularization, and so on.
[Optional for PARTITION BY ANY] The MetaInformationTable output schema is as follows:
MetaInformationTable Output Schema for PARTITION BY ANY
Column Data Type Description
iteration INTEGER Iteration number.
num_rows BIGINT Total number of rows processed.
eta FLOAT Learning rate for the iteration.
loss FLOAT Loss in the iteration.
best_loss FLOAT Best loss until the specified iteration.
Step INTEGER [StepwiseDirection only] Iteration number (step number).
SubStep INTEGER [StepwiseDirection only] Feature number to be added or deleted. Non-feature numbers are given to different algorithm stages for sorting purposes.
Description VARCHAR [StepwiseDirection only] Description of the stages of each step. Added features are preceded by a plus sign, and deleted features by a minus sign.
Score FLOAT [StepwiseDirection only] Score of a given model tested in each substep, as well as the best score in each step and the best overall score, as indicated by the Description column.
Model VARCHAR [StepwiseDirection only] List of variable names that are contained in the model at each step.
The output schema for partition by key is as follows:
Output Schema for Partition by Key
Column Data Type Description
partition_by_column CHARACTER, VARCHAR,INTEGER, BIGINT, SMALLINT, BYTEINT Data type is the same as the original column in the input table.
attribute SMALLINIT Numeric index of predictor and model metrics. Intercept is specified using index 0, and the rest of the predictors take positive values. Model metrics take negative indices.
predictor VARCHAR Name of the predictor or model metric.
estimate FLOAT Predictor weights and numeric-based metric values.
value VARCHAR String-based metric values, such as SQUARED_ERROR for LossFunction, L2 for Regularization, and so on.