Develop a decision tree-based classification model using the hpsplit procedure of SAS.

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

1. Download the breast-cancer-dataset.csv from your D2L Assignment 1 link. Complete the following tasks (5 points):

a. Read the file in SAS and display the contents using the import and print procedures. (1 point)

b. Develop a decision tree-based classification model using the hpsplit procedure of SAS. (2 points)

c. Navigate the contents of Results View by clicking on HPSplit breast- cancer-dataset, and then by selecting Model Assessment. Examine the confusion matrix, fit statistics, and variable importance. (2 points)

2. Using the confusion matrix, compute the following assessment metrics accuracy, recall, and precision (see lecture for formulas and state your assumption in bold which class in the confusion matrix you want to consider positive--i.e., recurrence-event or not-recurrence-event). (5 points)

Condition for marks: 3 points for accuracy, 1 point for precision, and 1 point for recall.

3. Change the grow algorithm to "gini" and recompute the metrics from question 2. Does entropy build a more accurate classifier or gini? (5 points)

Reference: UCI Machine Learning Repository [Breast-cancer dataset]

 

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