# Lesson 19: Data Scientists or Doctors?

## Lesson 19: Data Scientists or Doctors?

### Objective:

Students will create their own decision trees based on training data (i.e., the data from the previous day's lessons), and then see how well their decision tree works on new test data.

### Materials:

1. Decision Tree for Heart Attack Risk graphic (LMR_4.22_CART Heart Attacks)

### Vocabulary:

training data, testing data

### Essential Concepts:

Essential Concepts:

We can determine the usefulness of decision trees by comparing the number of misclassifications in each.

### Lesson:

1. Ask students the following question:

If a close friend or family member were having chest pains, would you want to take that person to a doctor or to a data scientist?

2. Give the students some time to think about the question and have a few of them share out their responses with the class.

Note: It’s likely that most students will choose to bring their loved one to a doctor.

3. As it turns out, back in the late 1970s, a cardiologist (and early data scientist) named Lee Goldman developed a decision tree based on millions of patient observations. The decision tree was made to diagnose whether people were or were not having a heart attack. Interestingly, the results of the decision tree compared to how actual doctor diagnoses are shown below:

1. Correct diagnoses using the decision tree were above 95%.

2. Correct diagnoses based on individual doctors’ expertise? Anywhere between 75-90%.

4. Display the graphic from the Decision Tree for Heart Attack Risk file (LMR_4.22_CART Heart Attacks) and explain that this is one example of what the decision tree might have looked like.

Note: This is NOT the actual tree Goldman developed.

5. Using a Pair-Share, ask students to discuss the following questions using the graphic above, as well as what they learned during the previous lesson’s activity.

a. What are decision trees?

b. How do they work at classifying data into groups?

6. Then display the following data (the same data from the player cards used in the previous lesson):

Team Player Height (inches) Weight (pounds) Age League
Carolina Cam Newton 77 245 26 NFL
Chicago Sean Johnson 75 217 26 USMNT
Dallas Matt Cassel 76 230 33 NFL
Dallas Tony Romo 74 230 35 NFL
Dallas Matt Hedges 76 190 25 USMNT
Kansas City Alex Smith 76 216 31 NFL
Kansas City Matt Besler 72 170 28 USMNT
New England Tom Brady 76 225 38 NFL
New England Jermaine Jones 72 179 34 USMNT
Seattle Russell Wilson 71 206 27 NFL
Seattle Clint Dempsey 73 170 32 USMNT
Toronto Michael Bradley 73 179 28 USMNT
Toronto Jozy Altidore 73 174 26 USMNT
Washington, D.C. Robert Griffin III 74 223 25 NFL
Washington, D.C. Steve Birnbaum 74 181 28 USMNT
7. Distribute the Make Your Own Decision Tree handout (LMR_4.23_Your Own Decision Tree) and give students time to come up with their own decision trees based on the training data they are given. Students may work in pairs or teams. They should follow the directions on page 1 of the handout and come up with a series of possible yes/no questions that they could ask to classify each player into his correct league (the NFL or the USMNT).

8. Once the students have finished creating their decision trees, ask the following questions:

1. Will you be able to classify other players from a new data set correctly using this particular decision tree?

2. Is this decision tree too specific to the training data?

9. Inform the students that they should now use the testing data on page 2 of the handout to try to classify 5 mystery players into one of the two leagues. They should record the classification that their tree outputs in the data table on page 2.

10. Let the students compare their decision trees and league assignments with one another. Hopefully, there will be a bit of variety in terms of the trees and the classifications.

11. Next, show students the correct league classifications for the 5 mystery players. The mystery player names are also included in this table.

Team Player Height (inches) Weight (pounds) Age League
Toronto Michael Bradley 74 175 28 USMNT
New York Eli Manning 76 218 34 NFL
New Orleans Drew Brees 72 209 36 NFL
Washington, DC Perry Kitchen 72 160 23 USMNT
New England Lee Nguyen 68 150 29 USMNT
12. By a show of hands, ask:

a. How many students misclassified all of the players in the testing data?

b. How many misclassified 4 of the 5 players?

c. How many misclassified 3 of the 5 players?

d. How many misclassified 2 of the 5 players?

e. Did anyone correctly classify ALL 5 mystery players? If so, ask those students to share their decision trees with the rest of the class.

13. Inform students that, when faced with much more data, creating classification trees becomes much harder to make by hand. It is so difficult, in fact, that data scientists rely on software to grow their trees for them. Students will learn how to create decision trees in RStudio during the next lab.

### Class Scribes:

One team of students will give a brief talk to discuss what they think the 3 most important topics of the day were.

### Homework & Next Day

Write a paragraph describing the role testing data and training data play in creating a classification tree.

LAB 4G: Growing Trees

Complete Lab 4G prior to Lesson 20.