32.7 DBMS_DATA_MINING.Apply

The APPLY procedure in DBMS_DATA_MINING is a batch apply operation that writes the results of scoring directly to a table.

The columns in the table are mining function-dependent.

Scoring with APPLY generates the same results as scoring with the SQL scoring functions. Classification produces a prediction and a probability for each case; clustering produces a cluster ID and a probability for each case, and so on. The difference lies in the way that scoring results are captured and the mechanisms that can be used for retrieving them.

APPLY creates an output table with the columns shown in the following table:

Table 32-2 APPLY Output Table

Mining Function	Output Columns
classification	CASE_ID PREDICTION PROBABILITY
regression	CASE_ID PREDICTION
anomaly detection	CASE_ID PREDICTION PROBABILITY
clustering	CASE_ID CLUSTER_ID PROBABILITY
feature extraction	CASE_ID FEATURE_ID MATCH_QUALITY

Since APPLY output is stored separately from the scoring data, it must be joined to the scoring data to support queries that include the scored rows. Thus any model that is used with APPLY must have a case ID.

A case ID is not required for models that is applied with SQL scoring functions. Likewise, storage and joins are not required, since scoring results are generated and consumed in real time within a SQL query.

The following example illustrates Anomaly Detection with APPLY. The query of the APPLY output table returns the ten first customers in the table. Each has a a probability for being typical (1) and a probability for being anomalous (0).

Example 32-15 Anomaly Detection with DBMS_DATA_MINING.APPLY

EXEC dbms_data_mining.apply
        ('SVMO_SH_Clas_sample','svmo_sh_sample_prepared', 
         'cust_id', 'one_class_output'); 

SELECT * from one_class_output where rownum < 11;
 
   CUST_ID PREDICTION PROBABILITY
---------- ---------- -----------
    101798          1  .567389309
    101798          0  .432610691
    102276          1  .564922469
    102276          0  .435077531
    102404          1   .51213544
    102404          0   .48786456
    101891          1  .563474346
    101891          0  .436525654
    102815          0  .500663683
    102815          1  .499336317