Building And Interpreting

Decision Trees in Enterprise Miner

Getting Up 2 Speed

Open up the HMEQ project you worked on last class. You should drop 3 nodes in EM (Input, Insight, and Partition (to separate random training and validation)

K:/(common)/tsupra/MARK2042/

Building Decision Trees

Add a Tree Node Connect to Data

Partition Node

Check Status, Model Role and Measurement

Splitting Criteria: binary target variables default is

Ordinal target variables: must use Entropy or Gini. Here,We can use any of the three. These are typical statistical tests.See readings I handed out last class (WebCT).

Close Tree Node. Run it! View the results.

Tree with 18 leaves grown based on training data, pruned back to 8Based on validation. 8-leaf model has accuracy of 89.02% of theValidation set.

Choose View-Tree

10 leaves are visible here. New in EMVersion 8.

Tree Options…Follow the tasks below

Colours and Proportion of target value.

What did the 0 represent again? Leaves with all zeros willBe green. Individuals who will default on their loan will beRed.

Inspect for high percentage of bad loans (red) and good loans (green)

Change the Statistics

Find missing values

The branch that contains theValues greater than 45.1848 alsoContains the missing values

Select this tab next

View a path to the node

Right click an area

Using Tree Options – Default Tree

Add New Tree (Default)

Make 2 changes to the basic tab. GiveThis a max and a min set of values

2*25=50 is the RULE.

Add the Assessment nodeAnd connect.

Close and Save the changes

If you didn’t follow the RULE,You won’t be able to save.

View the results…

Run it.

View the tree again.

The defaulted tree diagram.

Is yoursDifferent?

Running The Assessment Node

Run the AssessmentNode

Select both the Trees

Interpretation

View a Lift Chart

Results!

Various Charts – what are they saying?

Further Study:

See WebCT for more resources. More information on Decision Trees. Assignment 4 also up on WebCT. Group Assignment will be delivered

next class.