PREDICTION_BOUNDS function is for use only with generalized linear models. It returns an object with two
UPPER. For a regression mining function, the bounds apply to value of the prediction. For a classification mining function, the bounds apply to the probability value. If the GLM was built using ridge regression, or if the covariance matrix is found to be singular during the build, then this function returns
NULL for both fields.
confidence_level, specify a number in the range (0,1). If you omit this clause, then the default value is 0.95.
class_value argument is valid for classification models but not for regression models. By default, the function returns the bounds for the prediction with the highest probability. You can use the
class_value argument to filter out the bounds value specific to a target value.
You can specify
class_value while leaving
confidence_level at its default by specifying
mining_attribute_clause has the same behavior for
PREDICTION_BOUNDS that it has for
PREDICTION. Refer to mining_attribute_clause.
Oracle Data Mining Administrator's Guide for information on the demo programs available in the code
Oracle Data Mining Application Developer's Guide for detailed information about real-time scoring with the Data Mining SQL functions
Oracle Database PL/SQL Packages and Types Reference for information on the
The following example returns the distribution of customers whose ages are predicted to be between 25 and 45 years with 98% confidence.
This example and the prerequisite data mining operations can be found in the demo file
$ORACLE_HOME/rdbms/demo/dmglcdem.sql. The example is presented here to illustrate the syntactic use of the function. General information on data mining demo files is available in Oracle Data Mining Administrator's Guide.
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