SAP HANA Predictive Analysis Library (PAL)

DROP TABLE PAL_C45_TREEMODEL_TBL;
CREATE COLUMN TABLE PAL_C45_TREEMODEL_TBL LIKE PAL_C45_TREEMODEL_T;
DROP TABLE PAL_C45_PMMLMODEL_TBL;
CREATE COLUMN TABLE PAL_C45_PMMLMODEL_TBL LIKE PAL_C45_PMMLMODEL_T;
CALL "DM_PAL".PAL_C45_PROC(PAL_C45_DATA_TBL, #PAL_CONTROL_TBL,
PAL_C45_TREEMODEL_TBL, PAL_C45_PMMLMODEL_TBL) with OVERVIEW;
SELECT * FROM PAL_C45_TREEMODEL_TBL;
SELECT * FROM PAL_C45_PMMLMODEL_TBL;
Expected Result
PAL_C45_TREEMODEL_TBL:
PAL_C45_PMMLMODEL_TBL:
3.2.4 CART Decision Tree
Classification and regression tree (CART) is used for classification or regression and only supports binary split.
CART is a recursive partitioning method similar to C4.5 decision tree. It uses GINI index or TWOING for
classification, and least square error for regression. In PAL, CART only supports the GINI split strategy. The
surrogate split method is used to support missing values when creating the tree model.
Prerequisites
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The target column of training data must not have null values, and other columns should have at least one
valid value (not null).
The table used to store the tree model is a column table.
144 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions