TD_SVM produces two outputs:
Model (Primary output): Contains the trained model along with model statics. The model stores the following model statistics:
- Loss Function
- MSE
- Number of Observations
- AIC
- BIC
- Number of Iterations
- Regularization
- Alpha (L1/L2/Elasticnet)
- Learning Rate (Initial)
- Learning Rate (Final)
- Momentum
- Nesterov
- LocalSGD Iterations
[Optional] MetaInformationTable (Secondary Output): Contains training progress information for each epoch.
Model Output Table Schema
| Column | Data Type | Description |
|---|---|---|
| attribute | SMALLINT | Contains a numeric index of predictor and model metrics. Intercept is specified using index 0 and rest of the predictors have positive values. Model metrics have negative indices. |
| predictor | VARCHAR | Contains the name of the predictor or model metric. |
| estimate | FLOAT | Contains the predictor weights and numeric-based metric values. |
| value | VARCHAR | Contains the string-based metric values for example, HINGE for LossFunction, L2 for Regularization, and so on. |
MetaInformationTable Output Table Schema [Optional]
| Column | Data Type | Description |
|---|---|---|
| iteration | INTEGER | Contains the iteration number (epoch number). |
| num_rows | BIGINT | Contains the total number of rows processed so far. |
| eta | FLOAT | Contains the learning rate for the iteration. |
| loss | FLOAT | Contains loss in this iteration. |
| best_loss | FLOAT | Contains best loss till this iteration. |