3.10.5 ML_PREDICT_TABLE

ML_PREDICT_TABLE generates predictions for an entire table of unlabeled data and saves the results to an output table. Predictions are performed in parallel.

ML_PREDICT_TABLE is a compute intensive process. Limiting operations to batches of 10 to 100 rows by splitting large tables into smaller tables is recommended.

A loaded model is required to run ML_PREDICT_TABLE. See Section 3.9.3, “Loading Models”.

ML_PREDICT_TABLE Syntax

CALL sys.ML_PREDICT_TABLE(table_name, model_handle, output_table_name);

ML_PREDICT_TABLE parameters:

• table_name: Specifies the fully qualified name of the input table (schema_name.table_name). The input table should contain the same feature columns as the training dataset but no target column.

• model_handle: Specifies the model handle or a session variable containing the model handle

• output_table_name: Specifies the table where predictions are stored. The table is created if it does not exist. A fully qualified table name must be specified (schema_name.table_name). If the table already exists, an error is returned.

Syntax Examples

• A typical usage example that specifies the fully qualified name of the table to generate predictions for, the session variable containing the model handle, and the fully qualified output table name:

  CALL sys.ML_PREDICT_TABLE('ml_data.iris_test', @iris_model, 
  'ml_data.iris_predictions');