Description of prediction_set.gif follows
Description of the illustration prediction_set.gif


Description of cost_matrix_clause.gif follows
Description of the illustration cost_matrix_clause.gif


Description of mining_attribute_clause.gif follows
Description of the illustration mining_attribute_clause.gif


This function is for use with classification models created using the DBMS_DATA_MINING package or with the Oracle Data Mining Java API. It is not valid with other types of models. It returns a varray of objects containing all classes in a multiclass classification scenario. The object fields are named PREDICTION, PROBABILITY, and COST. The datatype of the PREDICTION field depends on the target value type used during the build of the model. The other two fields are both Oracle NUMBER. The elements are returned in the order of best prediction to worst prediction.

  • For bestN, specify a positive integer to restrict the returned target classes to the N having the highest probability. If multiple classes are tied in the Nth value, the database still returns only N values. If you want to filter only by cutoff, specify NULL for this parameter.

  • For cutoff, specify a NUMBER value to restrict the returned target classes to those with a cost less than or equal to the specified cost value. You can filter solely by cutoff by specifying NULL for bestN.

    When you specify values for both bestN and cutoff, you restrict the returned predictions to only those that are the bestN and have a probability (or cost when COST MODEL is specified) surpassing the threshold.

  • Specify COST MODEL to indicate that the scoring should be performed by taking into account the cost matrix that was associated with the model at build time. If no such cost matrix exists, then the database returns an error.

    When you specify COST MODEL, both bestN and cutoff are treated with respect to the prediction cost, not the prediction probability. That is, bestN restricts the result to the target classes having the N best (lowest) costs, and cutoff restricts the target classes to those with a cost less than or equal to the specified cutoff.

    When you specify this clause, each object in the collection is a triplet of scalar values containing the prediction value (the datatype of which depends on the target value type used during model build), the prediction probability, and the prediction cost (both Oracle NUMBER).

    If you omit COST MODEL, each object in the varray is a pair of scalars containing the prediction value and prediction probability. The datatypes returned are as described in the preceding paragraph.

The mining_attribute_clause behaves as described for the PREDICTION function. Please refer to mining_attribute_clause.

See Also:


The following example lists, for ten customers, the likelihood and cost of using or rejecting an affinity card. This example has a binary target, but such a query is also useful in multiclass classification such as Low, Med, and High.

This example and the prerequisite data mining operations can be found in the demo file $ORACLE_HOME/rdbms/demo/dmdtdemo.sql. General information on data mining demo files is available in Oracle Data Mining Administrator's Guide. The example is presented here to illustrate the syntactic use of the function.

SELECT T.cust_id, S.prediction, S.probability, S.cost
  FROM (SELECT cust_id,
               PREDICTION_SET(dt_sh_clas_sample COST MODEL USING *) pset
          FROM mining_data_apply_v
         WHERE cust_id < 100011) T,
       TABLE(T.pset) S
ORDER BY cust_id, S.prediction;

---------- ---------- ----------- -----
    100001          0      .96682   .27
    100001          1      .03318   .97
    100002          0      .74038  2.08
    100002          1      .25962   .74
    100003          0      .90909   .73
    100003          1      .09091   .91
    100004          0      .90909   .73
    100004          1      .09091   .91
    100005          0      .27236  5.82
    100005          1      .72764   .27
    100006          0     1.00000   .00
    100006          1      .00000  1.00
    100007          0      .90909   .73
    100007          1      .09091   .91
    100008          0      .90909   .73
    100008          1      .09091   .91
    100009          0      .27236  5.82
    100009          1      .72764   .27
    100010          0      .80808  1.54
    100010          1      .19192   .81
20 rows selected.