Automatically Train and Prune a Decision Tree

Decision tree models are advantageous because they are conceptually easy to understand, yet they readily accommodate nonlinear associations between input variables and one or more target variables. They also handle missing values without the need for imputation. Therefore, you decide to first model the data using decision trees. You will compare decision tree models to other models later in the example.
SAS Enterprise Miner enables you to build a decision tree in two ways: automatically and interactively. You will begin by letting SAS Enterprise Miner automatically train and prune a tree.
To use the Decision Tree node to automatically train and prune a decision tree, complete the following steps:
  1. Select the Model tab on the Toolbar.
  2. Select the Decision Tree node icon. Drag the node into the Diagram Workspace.
  3. Connect the Replacement node to the Decision Tree node.
    Process Flow Diagram
  4. Select the Decision Tree node. In the Properties Panel, scroll down to view the Train properties:
    • Click on the value of the Maximum Depth splitting rule property, and enter 10. This specification enables SAS Enterprise Miner to train a tree that includes up to ten generations of the root node. The final tree in this example, however, will have fewer generations due to pruning.
    • Click on the value of the Leaf Size node property, and enter 8. This specification constrains the minimum number of training observations in any leaf to eight.
    • Click on the value of the Number of Surrogate Rules node property, and enter 4. This specification enables SAS Enterprise Miner to use up to four surrogate rules in each non-leaf node if the main splitting rule relies on an input whose value is missing.
    Note: The Assessment Measure subtree property is automatically set to Decision because you defined a profit matrix in Create a Data Source. Accordingly, the Decision Tree node will build a tree that maximizes profit in the validation data.
  5. In the Diagram Workspace, right-click the Decision Tree node, and select Run from the resulting menu. Click Yes in the confirmation window that opens.
  6. In the window that appears when processing completes, click Results. The Results window opens.
    1. On the View menu, select Modelthen selectEnglish Rules. The English Rules window opens.
    2. Expand the English Rules window. This window contains the IF-THEN logic that distributes observations into each leaf node of the decision tree.
      English Rules Window
      Notice that there are seven leaf nodes in this tree. For each leaf node, the following information is listed:
      • node number
      • number of training observations in the node
      • percentage of training observations in the node with TARGET_B=1 (did donate), adjusted for prior probabilities
      • percentage of training observations in the node with TARGET_B=0 (did not donate), adjusted for prior probabilities
      This tree has been automatically pruned to an optimal size. Therefore, the node numbers that appear in the final tree are not sequential. In fact, they reflect the positions of the nodes in the full tree, before pruning.
  7. Close the Results window.
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