SAP HANA Predictive Analysis Library (PAL)

PAL_MLR_RESULTS_TBL:
PAL_MLR_FITTED_TBL:
PAL_MLR_SIGNIFICANCE_TBL:
Example 2: Fitting multiple linear regression model with elastic net penalties
SET SCHEMA DM_PAL;
DROP TYPE PAL_ENET_MLR_DATA_T;
CREATE TYPE PAL_ENET_MLR_DATA_T AS TABLE( "ID" INT,"Y" DOUBLE,"V1" DOUBLE,"V2"
DOUBLE,"V3" DOUBLE);
DROP TYPE PAL_ENET_MLR_RESULT_T;
CREATE TYPE PAL_ENET_MLR_RESULT_T AS TABLE("Coefficient" varchar(50),
"CoefficientValue" DOUBLE);
DROP TYPE PAL_ENET_MLR_FITTED_T;
CREATE TYPE PAL_ENET_MLR_FITTED_T AS TABLE("ID" INT, "Fitted" DOUBLE);
DROP TYPE PAL_ENET_MLR_SIGNIFICANCE_T;
CREATE TYPE PAL_ENET_MLR_SIGNIFICANCE_T AS TABLE("NAME" varchar(50), "VALUE"
DOUBLE);
DROP TYPE PAL_ENET_MLR_PMMLMODEL_T;
CREATE TYPE PAL_ENET_MLR_PMMLMODEL_T AS TABLE("ID" INT, "Model" VARCHAR(5000));
DROP TYPE PAL_CONTROL_T;
CREATE TYPE PAL_CONTROL_T AS TABLE("NAME" VARCHAR(100),"INTARGS"
INT,"DOUBLEARGS" DOUBLE,"STRINGARGS" VARCHAR(100));
DROP TABLE PAL_ENET_MLR_PDATA_TBL;
272 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions