Skip Headers
Oracle® Database SQL Language Reference
12c Release 1 (12.1)

E17209-15
Go to Documentation Home
Home
Go to Book List
Book List
Go to Table of Contents
Contents
Go to Index
Index
Go to Master Index
Master Index
Go to Feedback page
Contact Us

Go to previous page
Previous
Go to next page
Next
PDF · Mobi · ePub

PREDICTION_BOUNDS

Syntax

Description of prediction_bounds.gif follows
Description of the illustration prediction_bounds.gif

mining_attribute_clause::=

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

Purpose

PREDICTION_BOUNDS applies a Generalized Linear Model (GLM) to predict a class or a value for each row in the selection. The function returns the upper and lower bounds of each prediction in a varray of objects with fields UPPER and LOWER.

GLM can perform either regression or binary classification:

If the model was built using ridge regression, or if the covariance matrix is found to be singular during the build, then PREDICTION_BOUNDS returns NULL for both bounds.

confidence_level is a number in the range (0,1). The default value is 0.95. You can specify class_value while leaving confidence_level at its default by specifying NULL for confidence_level.

mining_attribute_clause

mining_attribute_clause identifies the column attributes to use as predictors for scoring. This clause behaves as described for the PREDICTION function. (Note that the reference to analytic syntax does not apply.) See "mining_attribute_clause::=".

See Also:

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.

Example

The following example returns the distribution of customers whose ages are predicted with 98% confidence to be greater than 24 and less than 46.

SELECT count(cust_id) cust_count, cust_marital_status
  FROM (SELECT cust_id, cust_marital_status
    FROM mining_data_apply_v
    WHERE PREDICTION_BOUNDS(glmr_sh_regr_sample,0.98 USING *).LOWER > 24 AND
          PREDICTION_BOUNDS(glmr_sh_regr_sample,0.98 USING *).UPPER < 46)
    GROUP BY cust_marital_status;
 
    CUST_COUNT CUST_MARITAL_STATUS
-------------- --------------------
            46 NeverM
             7 Mabsent
             5 Separ.
            35 Divorc.
            72 Married