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:
The bounds for regression refer to the predicted target value. The data type of UPPER
and LOWER
is the data type of the target.
The bounds for binary classification refer to the probability of either the predicted target class or the specified class_value
. The data type of UPPER
and LOWER
is BINARY_DOUBLE
.
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
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:
Oracle Data Mining User's Guide for information about scoring
Oracle Data Mining Concepts for information about Generalized Linear Models
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.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