SAP HANA Predictive Analysis Library (PAL)

INSERT INTO PAL_CART_DATA_TBL VALUES ('Overcast', 83, 78, 'No', 'Play');
INSERT INTO PAL_CART_DATA_TBL VALUES ('Overcast', 64, 65, 'Yes', 'Play');
INSERT INTO PAL_CART_DATA_TBL VALUES ('Overcast', 81, 75, 'No', 'Play');
INSERT INTO PAL_CART_DATA_TBL VALUES ('Rain', 71, 80, 'Yes', 'Do not Play');
INSERT INTO PAL_CART_DATA_TBL VALUES ('Rain', 65, 70, 'Yes', 'Do not Play');
INSERT INTO PAL_CART_DATA_TBL VALUES ('Rain', 75, 80, 'No', 'Play');
INSERT INTO PAL_CART_DATA_TBL VALUES ('Rain', 68, 80, 'No', 'Play');
INSERT INTO PAL_CART_DATA_TBL VALUES ('Rain', 70, 96, 'No', 'Play');
DROP TABLE #PAL_CONTROL_TBL;
CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_CONTROL_TBL(
"NAME" VARCHAR (100),
"INTARGS" INTEGER,
"DOUBLEARGS" DOUBLE,
"STRINGARGS" VARCHAR (100)
);
INSERT INTO #PAL_CONTROL_TBL VALUES ('THREAD_NUMBER',2,null,null);
INSERT INTO #PAL_CONTROL_TBL VALUES ('SPLIT_CRITERIA', 103, null, null);
INSERT INTO #PAL_CONTROL_TBL VALUES ('SPLIT_THRESHOLD', null, 0.001, null);
INSERT INTO #PAL_CONTROL_TBL VALUES ('MAX_DEPTH', 5, null, null);
INSERT INTO #PAL_CONTROL_TBL VALUES ('MIN_RECORDS_OF_PARENT', 2, null, null);
INSERT INTO #PAL_CONTROL_TBL VALUES ('MIN_RECORDS_OF_LEAF',1, null, null);
INSERT INTO #PAL_CONTROL_TBL VALUES ('PMML_EXPORT', 2, null, null);
DROP TABLE PAL_CART_TREEMODEL_TBL;
CREATE COLUMN TABLE PAL_CART_TREEMODEL_TBL LIKE PAL_CART_TREEMODEL_T;
DROP TABLE PAL_CART_STATISTIC_TBL;
CREATE COLUMN TABLE PAL_CART_STATISTIC_TBL LIKE PAL_CART_STATISTIC_T;
CALL "DM_PAL".PAL_CART_PROC(PAL_CART_DATA_TBL, #PAL_CONTROL_TBL,
PAL_CART_TREEMODEL_TBL, PAL_CART_STATISTIC_TBL) with OVERVIEW;
SELECT * FROM PAL_CART_TREEMODEL_TBL;
SELECT * FROM PAL_CART_STATISTIC_TBL;
Expected Result:
PAL_CART_TREEMODEL_TBL:
PAL_CART_STATISTIC_TBL:
3.2.5 CHAID Decision Tree
CHAID stands for CHi-squared Automatic Interaction Detection. It is similar to the C4.5 decision tree. CHAID is
a classification method for building decision trees by using chi-square statistics to identify optimal splits.
CHAID examines the cross tabulations between each of the input fields and the outcome, and tests for
significance using a chi-square independence test. If more than one of these relations is statistically
significant, CHAID will select the input field that is the most significant (smallest p value). CHAID can generate
non-binary trees.
150 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions