SAP HANA Predictive Analysis Library (PAL)

3.2.3 C4.5 Decision Tree
A decision tree is used as a classifier for determining an appropriate action or decision among a
predetermined set of actions for a given case. A decision tree helps you to effectively identify the factors to
consider and how each factor has historically been associated with different outcomes of the decision. A
decision tree uses a tree - like structure of conditions and their possible consequences. Each node of a
decision tree can be a leaf node or a decision node.
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Leaf node: mentions the value of the dependent (target) variable.
Decision node: contains one condition that specifies some test on an attribute value. The outcome of the
condition is further divided into branches with sub-trees or leaf nodes.
As a classification algorithm, C4.5 builds decision trees from a set of training data, using the concept of
information entropy. The training data is a set of already classified samples. At each node of the tree, C4.5
chooses one attribute of the data that most effectively splits it into subsets in one class or the other. Its
criterion is the normalized information gain (difference in entropy) that results from choosing an attribute for
splitting the data. The attribute with the highest normalized information gain is chosen to make the decision.
The C4.5 algorithm then proceeds recursively until meeting some stopping criteria such as the minimum
number of cases in a leaf node.
The C4.5 decision tree functions implemented in PAL support both discrete and continuous values. In PAL
implementation, the REP (Reduced Error Pruning) algorithm is used as pruning method.
Prerequisites
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The column order and column number of the predicted data are the same as the order and number used in
tree model building.
The last column of the training data is used as a predicted field and is of discrete type. The predicted data
set has an ID column.
The table used to store the tree model is a column table.
The target column of training data must not have null values, and other columns should have at least one
valid value (not null).
Note
C4.5 decision tree treats null as a special value.
CREATEDTWITHC45
This function creates a decision tree from the input training data.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘CREATEDTWITHC45’,
‘’, '', );
138 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions