Working with Decision Trees :: SAS® Visual Analytics 7.4: User's Guide

About Decision Trees

Note: If SAS Visual Statistics is licensed at your site, then the decision tree contains advanced features. See Decision Trees.

A decision tree uses the values of one or more predictor data items to predict the values of a response data item. A decision tree displays a series of nodes as a tree, where the top node is the response 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 response contains continuous data) or as a bar chart (if the response contains discrete data). The histogram or bar chart in each node displays the values of the response 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.

Below the decision tree, an icicle plot of the nodes is displayed. The color of the node in the icicle plot indicates the predicted level for that node. When you select a node in either the decision tree or the icicle plot, the corresponding node is selected in the other location.

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:

Response

specifies the response for the decision tree. You can specify any category or measure. The decision tree attempts to predict the values of the response data item. The bar chart or histogram inside each node of the tree displays the frequency of values for the response 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 or the contribution has been pruned, then the predictor is not included in the final tree that is displayed.

Specify Properties for a Decision Tree

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

Name

specifies the name of 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.

Custom

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

If you select Custom 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.

Response bins

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

Note: This option has no effect if the response 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.

Rapid growth

enables you to use the information gain ratio and k-means fast search methods for decision tree growth. When disabled, the information gain and greedy search methods are used, which generally produce a larger tree and require more time to create.

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
Response 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.

Derive a Leaf ID Data Item from a Decision Tree

You can derive a leaf ID data item to represent the results of a decision tree. The leaf ID data item creates values that correspond to the node IDs in the details table for the decision tree.

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

To calculate a leaf ID data item from a decision tree:

Select the drop-down list from the visualization toolbar, and then select Derive a Leaf ID Variable.
In the New Calculated Item window, enter a Name for the new calculated item.
Click OK to create the new data 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, right-click an empty part of the decision tree, and then select Show Overview.

Note: Each leaf node in the overview displays a single bar for the greatest value in that node. To display a bar for each node in the overview, right-click an empty part of the decision tree, and then select Show Branch Coloring.

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 you zoom out on the decision tree, then each leaf node displays a single bar for the greatest value in that node. To display a bar for each node in the tree, right-click an empty part of the decision tree, and then select Show Branch Coloring.

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