Use the training data to respond to the prompts below. Let’s decide what decision nodes to use in the 2nd level of our tree.
Shopping History as the Decision Node
1 Let’s look at the "shopping history" and "age" columns of the training dataset and see what we can learn about how customer’s purchasing habits correspond to their decision to buy.
previous customers in their teens: |
# who bought out of # in group individuals buy the game. |
new customers in their teens: |
# who bought out of # in group individuals buy the game. |
2 Let’s use what we’ve learned to write our rule.
Predict that previous teenage customers will / will not buy the game and new teenage customers will / will not buy the game.
3 Create a decision stump for "shopping history" with a root node of "teens". 4 Place a checkmark below each value that the computer would predict correctly.
5 A computer following this rule for our training data would make correct predictions out of 5 attempts ( % accuracy).
Interest in Game as the Decision Node
6 Let’s look at the "shopping history" and "age" columns of the training dataset and see what we can learn about how customer’s purchasing habits correspond to their decision to buy.
interested teens: |
# who bought out of # in group individuals buy the game. |
uninterested teens |
# who bought out of # ingroup individuals buy the game. |
7 Let’s use what we’ve learned to write our rule.
Predict that interested teenage shoppers will / will not buy the game and uninterested teenage shoppers will / will not buy the game.
8 Create a decision stump for teenagers' interest in the game. Place a checkmark below each value that the computer would predict correctly.
9 A computer following this rule for our training data would make correct predictions out of 5 attempts ( % accuracy).
What decision attribute should we use?
10 Will you use shopping history or interest for your second decision node? Explain your response.
These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, 1738598, 2031479, and 1501927). 
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