How to Build a Classification Model in CART: A Tutorial

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1 : A Tutorial This tutorial will teach you everything you need to know to build your first classification model using CART (Classification and Regression Trees). CART is the ultimate classification tree that has revolutionized the entire field of advanced analytics. You will be using a data set containing thousands of s and their characteristics where you will attempt to classify each as spam or not spam. Just open SPM and get started! 1. Locate the data set, spambase.csv, and click Open: File > Open > Data File

2 The Activity Window, pictured below, will appear: The variables listed on the left include characteristics such as average length of a word in all capitals, frequency of semicolons, frequency of the word project, and so on. On the right, you can see there are 4,601 records and 58 numeric variables. The variable TARGET is a binary variable indicating whether the is spam (1) or not spam (0); this is what you will be predicting. 2. At the bottom of the Activity Window, click the View Data button:

3 The window above will appear where you can simply view the data from the imported file in a spreadsheet to get an idea of the records and variable values. 3. You will now build the CART model. In the row of buttons at the bottom of the Activity Window, click Model:

4 4. The Model Setup window below will appear. In the Sort drop-down menu, choose File Order. 5. Find the TARGET variable in the Variable Selection pane and mark the corresponding check box in the Target column. Select the rest of the variables in the Predictor column. 6. In the Analysis Method drop-down menu, select CART. For Analysis Type, select Classification. The Model Setup window should now look like the figure above.

5 7. Click the Categorical tab at the top of the Model Setup window. Select the TARGET variable and click Set Class Names: 8. Click the TARGET variable in the resulting window and enter the display values, NO-SPAM and SPAM, for 0 and 1, respectively. This will make examining the created model easier. Click Done:

6 9. Click the Testing tab at the top of the Model Setup window: 10. Select Fraction of cases selected at random for testing and enter 0.50 in the box, as above. The data will be randomly partitioned into 50% for the training sample and 50% for the testing sample.

7 11. Click the Limits tab at the top of the Model Setup window: 12. In the Unweighted column, enter 60 for Parent node minimum cases and 20 for Terminal node minimum cases, as above. These numbers are a good starting point, but can be adjusted based on your data. 13. In the bottom right corner of the Model Setup window, click Start. The progress window below will pop up as the model builds: You have now successfully built a CART model! The remainder of the tutorial will guide you through examining the created sequence and making observations.

8 The above CART Navigator window will appear with the optimal tree (based on lowest relative cost; for the purpose of this tutorial, just know that models with a lower relative cost represent a better tradeoff between performance and complexity) shown in the upper pane and denoted by the green vertical bar in the lower pane. In this example, the optimal model is a tree with 12 terminal nodes. Click the blue squares in the bottom pane to see trees of different sizes, denoted by the red vertical bar:

9 Locate the smallest tree in the sequence and hover over the top node: The details in the box that appears show how the data was split. In this example, the data was split into records that had less than or equal to 0.01 frequency of exclamation marks and records that had greater than 0.01 frequency of exclamation marks. The resulting two nodes will contain details on these separate sets of data when you hover over or click them.

10 In the Navigator window, you can get more details on the selected model by clicking the Summary button in the bottom row: The Summary Results window above will give you many specific statistics about the model you selected. For example, the ROC Learn value for the tree with 2 terminal nodes is

11 At the top of the Summary Results window, click the Prediction Success tab: Here, you can see how well the model performed in predicting spam. The model was correct 74.85% of the time predicting non-spam s and correct 83.41% of the time predicting spam s. Other metrics include average percentage, specificity, and sensitivity. Return to the optimal model and click the Splitters button in the bottom row:

12 This option simply gives split-point information about the tree without giving any other details. You can also see whether the terminal nodes are spam-dominated (red) or non-spam-dominated (blue). This color-coding scale can be seen at the top of the CART Navigator window. Return to the CART Navigator window and click the small graph button to the left of the lower pane:

13 This button changes the graph to highlight other trees that are statistically within one standard deviation of the optimal tree. Here, you can see the trees with 15 and 16 nodes are now green and may be worth taking a closer look at. Try clicking this button again to bring up a bar chart of relative node sizes: From this bar chart, you can see most of the records fall into terminal nodes 1 and 10 in this tree.

14 Find and click Terminal Node 10 on the optimal tree: The Classification tab, below, gives a summary of the general makeup of Terminal Node 10, which we can see is predominantly spam records.

15 Click the Rules tab at the top of the window: The information in the red box above holds the characteristics of the records that fell into Terminal Node 10. All s in this node had exclamation mark frequency greater than 0.009, hp frequency less than or equal to 0.115, longest capital run length greater than 10.5, and edu frequency less than or equal to These s were subsequently labeled likely to be spam. You have now successfully built and analyzed a CART model! In the next tutorial, you will learn how to build and analyze a MARS (Multivariate Adaptive Regression Splines) model. Check out our Content Mastery Q&A below to test the skills you learned in this tutorial! Have questions? Click here for help with your problem!

16 : Content Mastery Q&A Q1: What is the ROC Test value for the smallest tree? A1: Q2: What is the average prediction success rate for the smallest model? A2: 79.13%

17 Q3: Which terminal node is the largest in the optimal tree? A3: Terminal Node 1 Q4: What percentage of Terminal Node 10 in the optimal model is spam? A4: 90.1% Challenge: What is the average prediction success rate for the optimal model? Answer: 90.71%

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