See Also:"Analytic Functions" for information on the syntax, semantics, and restrictions of
FEATURE_VALUE returns a feature value for each row in the selection. The value refers to the highest value feature or to the specified
feature_id. The feature value is returned as
FEATURE_VALUE can score the data in one of two ways: It can apply a mining model object to the data, or it can dynamically mine the data by executing an analytic clause that builds and applies one or more transient mining models. Choose Syntax or Analytic Syntax:
Syntax — Use the first syntax to score the data with a pre-defined model. Supply the name of a feature extraction model.
Analytic Syntax — Use the analytic syntax to score the data without a pre-defined model. Include
n is the number of features to extract, and
mining_analytic_clause, which specifies if the data should be partitioned for multiple model builds. The
mining_analytic_clause supports a
query_partition_clause and an
order_by_clause. (See "analytic_clause::=".)
mining_attribute_clause identifies the column attributes to use as predictors for scoring. When the function is invoked with the analytic syntax, this data is also used for building the transient models. The
mining_attribute_clause behaves as described for the
PREDICTION function. (See "mining_attribute_clause::=".)
About the Example:The following example is excerpted from the Data Mining sample programs. For more information about the sample programs, see Appendix A in Oracle Data Mining User's Guide.
The following example lists the customers that correspond to feature 3, ordered by match quality.
SELECT * FROM (SELECT cust_id, FEATURE_VALUE(nmf_sh_sample, 3 USING *) match_quality FROM nmf_sh_sample_apply_prepared ORDER BY match_quality DESC) WHERE ROWNUM < 11; CUST_ID MATCH_QUALITY ---------- ------------- 100210 19.4101627 100962 15.2482251 101151 14.5685197 101499 14.4186292 100363 14.4037396 100372 14.3335148 100982 14.1716545 101039 14.1079914 100759 14.0913761 100953 14.0799737