IDAX.FPR - Calculate the false positive rate from a confusion matrix

Use this stored procedure to calculate the false positive rate of a class from a confusion matrix. The false positive rate is the ratio of the number of wrongly classified records from the specified class that is divided by the number of real values that are not this class.

Authorization

The privileges held by the authorization ID of the statement must include the IDAX_USER role.

Syntax

IDAX.FPR(in parameter_string varchar(32672))

Parameter descriptions

parameter_string

Mandatory one-string parameter that contains pairs of <parameter>=<value> entries that are separated by a comma.

Data type: VARCHAR(32672)

The following list shows the parameter values:

matrixTable: Mandatory.; The name of the confusion matrix table.; Data type: VARCHAR(256)
class: Mandatory.; The class in the confusion matrix table for which the false positive rate is calculated.; Data type: VARCHAR(ANY)

Returned information

Double the false positive rate as a result set.

Example

CALL IDAX.SPLIT_DATA('intable=samples.customer_churn,traintable=cust_train,testtable=cust_test,id=cust_id,fraction=0.30');
CALL IDAX.GROW_DECTREE('model=cust_dt, intable=cust_train, id=cust_id, target=censor, incolumn=duration:ignore;censor:nom, minsplit=2');
CALL IDAX.PREDICT_DECTREE('model=cust_dt, intable=cust_test, outtable=cust_dt_out');
call IDAX.CONFUSION_MATRIX('intable=cust_test, id=cust_id, target=censor, resulttable=cust_dt_out, matrixtable=cust_dt_cm');
call IDAX.FPR('matrixTable=cust_dt_cm, class=0');