Working with Decision Trees :: SAS(R) Visual Analytics 7.1: User's Guide

About Decision Trees

A decision tree uses the values of one or more predictor data items to predict the values of a target data item. A decision tree displays a series of nodes as a tree, where the top node is the target data item, and each branch of the tree represents a split in the values of a predictor data item. Decision trees are also known as classification and regression trees.

Example Decision Tree

Each branch of the tree displays the name of the predictor for the branch at the top of the split. The thickness of the branch indicates the number of values that are associated with each node. The predictor values for each node are displayed above the node.

Each node in the tree displays the data for the node either as a histogram (if the target contains continuous data) or as a bar chart (if the target contains discrete data). The histogram or bar chart in each node displays the values of the target data item that are selected by the splits in the tree. The number at the top right of the node indicates the greatest value or frequency for the bar chart or histogram. At the bottom of each node, the total number of data values (count) for the node is displayed.

Decision trees in SAS Visual Analytics use a modified version of the C4.5 algorithm.

The details table for a decision tree contains two additional data columns, Node ID and Parent ID. Node ID specifies a unique value for each node in the tree. Parent ID specifies the ID of the parent node.

Data Roles for a Decision Tree

The basic data roles for a decision tree are:

Target

specifies the target for the decision tree. You can specify any category or measure. The decision tree attempts to predict the values of the target data item. The bar chart or histogram inside each node of the tree displays the frequency of values for the target data item.

Predictors

specifies predictors for the decision tree. You can specify one or more categories or measures as predictors. The values of predictor data items are displayed above the nodes in the tree. The order of the data items in the Predictors list does not affect the tree.

Note: If a predictor does not contribute to the predictive accuracy of the tree, then it is not used in the tree.

Specify Properties for a Decision Tree

On the Properties tab, you can specify the following options:

Name

specifies the name of the visualization.

Title

specifies the title that appears above the graph.

Note: The Title option is disabled if you select Generate graph title.

Generate graph title

specifies that the graph title is generated automatically based on the data items in the visualization.

Include missing

specifies whether missing values are included in the tree.

Frequency

specifies whether the frequency value for each node is displayed as a count (Count) or as a percentage (Percent).

Note: The frequency values are based on the data that is shown in the visualization (after filters and other data selections have been applied).

Growth strategy

specifies the parameters that are used to create the decision tree. Select one of the following values:

Basic

specifies a simple tree with a maximum of two branches per split and a maximum of six levels. For details, see Parameter Values for the Basic and Advanced Growth Strategies.

Advanced

specifies a complex tree with a maximum of four branches per split and a maximum of six levels. For details, see Parameter Values for the Basic and Advanced Growth Strategies.

Expert

enables you to select the values for each of the expert parameters.

If you select Expert as the value for Growth strategy, then the following additional options appear:

Maximum branches

specifies the maximum number of branches for each node split.

Maximum levels

specifies the maximum number of levels in the tree.

Leaf size

specifies the minimum number of values (count) for each node.

Target bins

specifies the number of bins that are used for the target data item.

Note: This option has no effect if the target data item contains discrete data.

Predictor bins

specifies the number of bins that are used for predictor data items.

Note: This option has no effect if the predictor data items contain discrete data.

Pruning

specifies the level of pruning that is applied to the tree. Pruning removes leaves and branches that contribute the least to the predictive accuracy of the tree. A more Lenient pruning value specifies that fewer leaves and branches are removed from the tree. A more Aggressive pruning value specifies that more leaves and branches are removed from the tree.

Reuse predictors

specifies that predictors can be used more than once in the tree.

The following parameter values are used for the Basic and Advanced growth strategies:

Parameter Values for the Basic and Advanced Growth Strategies
Property	Basic Value	Advanced Value
Maximum branches	2	4
Maximum levels	6	6
Leaf size	1	1
Target bins	10	10
Predictor bins	2	10
Reuse predictors	No	Yes

Explore a Node as a New Visualization

For each node in the tree, you can explore the data values as a new histogram or bar chart visualization. To create a new visualization for a node, right-click on the node, and then select Create Visualization from Node. A new visualization appears.

Note: If you create a visualization from a node that displays a histogram, then the new histogram might have visual differences from the histogram that is displayed in the decision tree. For example, the bins and the extent of data on the X axis might be different. However, the two histograms use exactly the same data.

Calculate a Data Item from a Decision Tree

You can create a calculated data item to represent the results of a decision tree. The calculated data item creates values that correspond to the node IDs that can be seen in the details table for the decision tree.

You can use the calculated data item in filters to select the values for a decision tree node in other types of visualizations.

To calculate a data item from a decision tree:

Select the drop-down list from the visualization toolbar, and then select Create Calculated Item.
In the New Calculated Item window, enter a Name for the new calculated item.
Click OK to create the new item.

Display the Overview

For large decision trees, the overview enables you to select the portions of the tree that are visible.

To display the overview, select the Options

drop-down list from the visualization toolbar, and then select Show Overview.

Zoom a Decision Tree

You can zoom a decision tree by using either of the following controls:

in the overview, select the part of the decision tree that you want to view
scroll the mouse wheel over the visualization to zoom in or out at the location of the cursor

Note: If the target contains discrete data, and you zoom out on the decision tree, then each node displays a single bar for the greatest value in that node.

When you have zoomed in on a decision tree and scroll bars are displayed, you can reposition the decision tree by holding down the Shift and Alt keys and dragging it.

Pan (Scroll) a Decision Tree

You can pan (scroll) the decision tree by using any of the following controls:

in the overview, drag the selection box
hold down the Shift key and drag the decision tree