Chapter 4 Questions

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Apr 3, 2024

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4.4 Decision Trees in SAS Visual Statistics 4-89 r@ Practice '@ In this practice, you continue to use the PVA data set to build a decision tree to classify those customers who donated. 4. Building a Decision Tree in SAS Visual Statistics Return to your remote desktop client machine. If your session timed out, sign in. Use Student as the user ID and Metadata0 as the password. Start Visual Analytics or start a new report. Then select and open the PVA data source. Add a decision tree to the canvas. If you did not already do so, in the Measure column, edit Target Gift Flag and select Category to create a binary target variable for donations. Disable auto-refresh on the menu bar. Add Target Gift Flag as the response. Under Predictors, click Add. In the Add Data Items window, select all 28 predictor variables except for these four: Control Number Demographic Cluster Target Gift Amount Target Gift Amount with Zero (You add 24 columns.) Create the decision tree by enabling the auto-refresh. e How many customers made donations? o What proportion of individuals does that represent in the target node? e Why is this response profile different from when you built the logistic regression model? Zoom in toward the root node. ¢ On what column does the top split occur? What is the split point that determines to which branch a customer belongs? In which branch does the maijority of customers fall at this split point? How many customers were less than this value and belong to Node 27? What proportion of the customers in Node 2 made donations? 5. Examining Additional Decision Tree Results Open the summary table to examine the node statistics. 1) Examine the last column to see whether there are any 100% donor nodes. If so, which node or nodes? 2) Click the Node Rules tab. Is Node 27 a class node or a leaf node? Copyright © 2020, SAS Institute Inc., Cary, North Carolina, USA. ALL RIGHTS RESERVED.

4-90 Lesson 4 Models with Categorical Targets 3) Can you use the node rules to describe the customers in Node 27 in laymen’s terms? 4) Examine the Variable Importance table to see whether home ownership appears to be an important factor when you classify customers who make a donation. b. Close the details table. c. Maximize the Assessment window and change the variable importance plot to leaf statistics. 1) Other than the two 100% nodes, which node has the next highest percentage of donors? 2) Which leaf has the most customers? 3) Select the node with the most customers and change the chart back to percent to determine the proportion of donors. 4) Examine the lift chart. What can you determine about the top 10% (percentile) of the data? d. Save your report as Practice 7. End of Practices Copyright © 2020, SAS Institute Inc., Cary, North Carolina, USA. ALL RIGHTS RESERVED.

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