Supported SQL Scoring Functions

Supported scoring functions for in-database scoring of machine learning models imported in the ONNX format are listed.

Machine Learning Technique	Operator	Supported	Return Type
Embedding	VECTOR_EMBEDDING	always	VECTOR(<dimensions , FLOAT32>) The number of dimensions of the output vector of a VECTOR_EMBEDDING operator is defined by the embedding models.
Regression	PREDICTION	always	Data type of the target. For regression, the data type is converted to BINARY_DOUBLE SQL type.
Classification	PREDICTION	always	Data type of the target.
Classification	PREDICTION_PROBABILITY	always	BINARY_DOUBLE
Classification	PREDICTION_SET	always	Set of ( t, NUMBER , BINARY_DOUBLE ) where t is the data type of the target.
Clustering	CLUSTER_ID	only if clusteringProbOutput is specified	NUMBER
Clustering	CLUSTER_PROBABILITY	only if clusteringProbOutput is specified	BINARY_DOUBLE
Clustering	CLUSTER_SET	only if clusteringProbOutput is specified	Set of ( NUMBER, BINARY_DOUBLE )
Clustering	CLUSTER_DISTANCE	only if clusteringDistanceOutput is specified	BINARY_DOUBLE

Note:

You can define the outputs explicitly in the metadata or implicitly.

• The metadata must explicitly specify how to find the result in the model output for some SQL scoring functions. For example, CLUSTER_PROBABILITY is supported only if clusteringProbOutput is specified in the metadata.

• The system automatically assumes the output for a model with only one output if you don't specify it in the metadata.

• If a scoring function does not comply according to the description provided, you will receive an ORA-40290 error when performing the scoring operation on your data. Additionally, any unsupported scoring functions will raise the ORA-40290 error.

To learn more about classification data types that are returned, see labels and classificationLabelOutput in JSON Metadata Parameters for ONNX Models.

Cost Matrix Clause

Specify a cost matrix directly within the PREDICTION and PREDICTION_SET scoring functions. To learn more about Cost Matrix, see Oracle Machine Learning for SQL Concepts.