Sas Enterprise Miner Decision Tree Interactive Continue to Edit

Chapter 5

Build Decision Trees

About the Tasks That You Will Perform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Automatically Train and Prune a Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Interactively Train a Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Create a Gradient Boosting Model of the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

About the Tasks That You Will Perform

Now that you have verified the input data, it is time to build predictive models. You

perform the following tasks to model the input data using nonparametric decision trees:

1. You enable SAS Enterprise Miner to automatically train a full decision tree and to

automatically prune the tree to an optimal size. When training the tree, you select

split rules at each step to maximize the split decision logworth. Split decision

logworth is a statistic that measures the effectiveness of a particular split decision at

differentiating values of the target variable. For more information about logworth,

see the SAS Enterprise Miner Help.

2. You interactively train a decision tree. At each step, you select from a list of

candidate rules to define the split rule that you deem to be the best.

3. You use a Gradient Boosting node to generate a set of decision trees that form a

single predictive model. Gradient boosting is a boosting approach that resamples the

analysis data set several times to generate results that form a weighted average of the

re-sampled data set.

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.

However, before you add and run the Decision Tree node, you will add a Control Point

node. The Control Point node is used to simplify a process flow diagram by reducing the

number of connections between multiple interconnected nodes. By the end of this

23

example, you will have created five different models of the input data set, and two

Control Point nodes to connect these nodes. The first Control Point node, added here,

will distribute the input data to each of these models. The second Control Point node will

collect the models and send them to evaluation nodes.

To use the Control Point node:

1. Select the Utility tab on the Toolbar.

2. Select the Control Point node icon. Drag the node into the Diagram Workspace.

3. Connect the Replacement node to the Control Point node.

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:

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 Control Point node to the Decision Tree node.

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

24 Chapter 5 • Build Decision Trees

elliscoich1962.blogspot.com

Source: https://www.oreilly.com/library/view/getting-started-with/9781612905525/chapter-18.html