Show the implementation of Decision Tree.

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