FEATURE_VALUE

Syntax

feature_value::=

Description of feature_value.eps follows
Description of the illustration feature_value.eps

Analytic Syntax

feature_value_analytic::=

Description of feature_value_analytic.eps follows
Description of the illustration feature_value_analytic.eps

mining_attribute_clause::=

Description of mining_attribute_clause.eps follows
Description of the illustration mining_attribute_clause.eps

mining_analytic_clause::=

Description of mining_analytic_clause.eps follows
Description of the illustration mining_analytic_clause.eps

See Also:

"Analytic Functions" for information on the syntax, semantics, and restrictions of mining_analytic_clause

Purpose

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 BINARY_DOUBLE.

Syntax Choice

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 INTO n, where 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::=".)

The syntax of the FEATURE_VALUE function can use an optional GROUPING hint when scoring a partitioned model. See GROUPING Hint.

mining_attribute_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::=".)

See Also:

Note:

The following example is excerpted from the Oracle Machine Learning for SQL sample programs. For more information about the sample programs, see Appendix A in Oracle Machine Learning for SQL User’s Guide.

Example

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