Database Reference
In-Depth Information
our label attribute that follow the branch of the tree to the point of that leaf. We can see in
this tree that Website_Activity is our best predictor of whether or not a customer is going
to adopt (buy) the company's new eReader. If the person's activity is frequent or regular,
we see that they are likely to be an Innovator or Early Adopter, respectively. If however,
they seldom use the web site, then whether or not they've bought digital books becomes
the next best predictor of their eReader adoption category. If they have not bought digital
topics through the web site in the past, Age is another predictive attribute which forms a
node, with younger folks adopting sooner than older ones. This is seen on the branches
for the two leaves coming from the Age node in Figure 10-6. Those who seldom use the
company's website, have never bought digital topics on the site, and are older than 25 ½
are most likely to land in the Late Majority category, while those with the same profile but
are under 25 ½ are bumped to the Early Majority prediction. In this example you can see
how you read the nodes, leaves and branch labels as you move down through the tree.
Before returning to design perspective, take a minute to try some of the tools on the left
hand side of the screen. The magnifying glasses can help you see your tree better,
spreading out or compacting the nodes and leaves to enhance readability or to view more
of a large tree at one time. Also, try using the 'hand' icon under Mode (see the arrow on
Figure 10-6). This allows you to click and hold on individual leaves or nodes and drag
them around to enhance your tree's readability. Finally, try hovering your mouse over one
of the leaves in the tree. In Figure 10-7, we see a tool-tip hover box showing details of this
leaf. Although our training data is going to predict that 'regular' web site users are going to
be Early Adopters, the model is not 100% based on that prediction. In the hover, we read
that in the training data set, 9 people who fit this profile are Late Adopters, 58 are
Innovators, 75 are Early Adopters and 41 are Early Majority. When we get to Evaluation
phase, we will see that this uncertainty in our data will translate into confidence
percentages, similar to what we saw in Chapter 9 with logistic regression.
