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 ] MetaInformationTable (Secondary Output): Contains training progress information for each iteration.
The model output schema for partition by any is as follows:
| 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] The MetaInformationTable output schema is as follows:
| 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. |