Data Mining SQL Scoring Functions

A set of specialized SQL functions provides the primary mechanism for scoring data in Oracle Data Mining. When called as single-row functions, the SQL Data Mining functions apply a user-supplied mining model to each row of input data.

In Oracle Database 12c, the functions can also be called as analytic functions, in which case the algorithmic processing is performed dynamically without a user-supplied mining model. The term predictive query refers to this mode of scoring.

Table 1-2 Data Mining SQL Scoring Functions

Function Name	Function Description
`CLUSTER_DETAILS`	Returns cluster details for each row in the input data.
`CLUSTER_DISTANCE`	Returns the distance between each row and the centroid.
`CLUSTER_ID`	Returns the ID of the highest probability cluster for each row.
`CLUSTER_PROBABILITY`	Returns the highest probability cluster for each row.
`CLUSTER_SET`	Returns a set of cluster ID and probability pairs for each row.
`FEATURE_DETAILS`	Returns a set of feature and value paris for each row.
`FEATURE_ID`	Returns feature details for each row in the input data.
`FEATURE_SET`	Returns a set of feature ID and feature value pairs for each row.
`FEATURE_VALUE`	Returns the value of the highest value feature for each row
`PREDICTION`	Returns the prediction for each row in the input.
`PREDICTION_BOUNDS`	Returns the upper and lower bounds of prediction for each row (GLM only).
`PREDICTION_COST`	Returns a cost for each row.
`PREDICTION_DETAILS`	Returns prediction details for each row.
`PREDICTION_PROBABILITY`	Returns the probability of each prediction.
`PREDICTION_SET`	Returns the prediction or cost with probability for each row.

Note:

The SQL scoring functions are available in code snippets in Oracle Data Miner, as described in Oracle Data Miner User's Guide.