The content discusses the concept of Area Under Curve (AUC) as a method to evaluate the performance of classification algorithms. AUC is typically used for binary classifiers but can also be extended to multiple-class conditions. The AUC is calculated by plotting the Receiver Operating Characteristic (ROC) curve, which shows the true positive rate (TPR) against the false positive rate (FPR) at different thresholds. The AUC value ranges from 0.5 to 1, with 1 indicating perfect performance. The AUC can be calculated using numerical integral algorithms such as Simpson's rule.
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SET SCHEMA DM_PAL;

DROP TABLE PAL_AUC_DATA_TBL;
CREATE COLUMN TABLE PAL_AUC_DATA_TBL(
    ID INTEGER, 
    ORIGINAL INTEGER,
    PREDICT DOUBLE
);
INSERT INTO PAL_AUC_DATA_TBL VALUES(1,0,0.07);
INSERT INTO PAL_AUC_DATA_TBL VALUES(2,0,0.01);
INSERT INTO PAL_AUC_DATA_TBL VALUES(3,0,0.85);
INSERT INTO PAL_AUC_DATA_TBL VALUES(4,0,0.3);
INSERT INTO PAL_AUC_DATA_TBL VALUES(5,0,0.5);
INSERT INTO PAL_AUC_DATA_TBL VALUES(6,1,0.5);
INSERT INTO PAL_AUC_DATA_TBL VALUES(7,1,0.2);
INSERT INTO PAL_AUC_DATA_TBL VALUES(8,1,0.8);
INSERT INTO PAL_AUC_DATA_TBL VALUES(9,1,0.2);
INSERT INTO PAL_AUC_DATA_TBL VALUES(10,1,0.95);

DROP TABLE #PAL_PARAMETER_TBL;
CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_PARAMETER_TBL(
    "PARAM_NAME" NVARCHAR(256),
    "INT_VALUE" INTEGER, 
    "DOUBLE_VALUE" DOUBLE, 
    "STRING_VALUE" NVARCHAR (1000)
);
INSERT INTO #PAL_PARAMETER_TBL VALUES('POSITIVE_LABEL',NULL,NULL,'1');

CALL _SYS_AFL.PAL_AUC(PAL_AUC_DATA_TBL,#PAL_PARAMETER_TBL,?,?);

