Show the implementation of Decision Tree.
A decision
trees model has a single parent node that represents the model and its
metadata. Underneath the parent node are independent trees that represent the
predictable attributes that you select. For example, if you set up your
decision tree model to predict whether customers will purchase something, and
provide inputs for gender and income, the model would create a single tree for
the purchasing attribute, with many branches that divide on conditions related
to gender and income.
However, if you
then add a separate predictable attribute for participation in a customer
rewards program, the algorithm will create two separate trees under the parent
node. One tree contains the analysis for purchasing, and another tree contains
the analysis for the customer rewards program. If you use the Decision Trees
algorithm to create an association model, the algorithm creates a separate tree
for each product that is being predicted, and the tree contains all the other
product combinations that contribute towards selection of the target attribute.
The tree for each predictable attribute contains information that describes how
the input columns that you choose affect the outcome of that particular
predictable attribute. Each tree is headed by a node (NODE_TYPE = 9) that
contains the predictable attribute, followed by a series of nodes (NODE_TYPE =
10) that represent the input attributes. An attribute corresponds to either a
case-level column or values of nested table columns, which are generally the
values in the Key column of the nested table.
Interior and
leaf nodes represent split conditions. A tree can split on the same attribute
multiple times. For example, the TM_DecisionTree model might split on [Yearly Income] and
[Number of Children], and then split again on [Yearly Income] further down the
tree.
Data Required
for Decision Tree Models
When you
prepare data for use in a decision trees model, you should understand the
requirements for the particular algorithm, including how much data is needed,
and how the data is used.
The
requirements for a decision trees model are as follows:
A single key
column : Each model must contain one
numeric or text column that uniquely identifies each record. Compound keys are
not permitted.
A predictable
column : Requires at least one
predictable column. You can include multiple predictable attributes in a model,
and the predictable attributes can be of different types, either numeric or
discrete. However, increasing the number of predictable attributes can increase
processing time.
Input
columns : Requires input columns, which
can be discrete or continuous. Increasing the
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