TD_GLM Output - Teradata Vantage

Teradata® VantageCloud Lake
Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
ft:locale
en-US
ft:lastEdition
2024-12-11
dita:mapPath
phg1621910019905.ditamap
dita:ditavalPath
pny1626732985837.ditaval
dita:id
phg1621910019905
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.
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.
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.