Working with a decision tree for the Acute inflammations dataset from UCI
A decision tree is a graphical way of representing knowledge. As its name implies, it's a tree-like structure that shows decisions about something, and it’s useful in many fields, from management to medicine.
Think of a decision tree as a way to structure a sequence of questions and possible answers. One prominent use of a decision tree is to show the flow of decision-making to a nontechnical audience.
Decision Tree Components
Figure 7-1 shows a decision tree for classifying irises along with decision tree terminology. You might recall from Chapter 6 that the iris dataset (downloaded from the UCI Machine Learning (ML) Repository and designated as iris.uci) consists of 150 rows and 5 columns. The 150 rows represent individual flowers, with 50 each of the setosa, versicolor, and virginica species. The five columns are sepal.length, sepal.width, petal.length, petal.width, and species.
FIGURE 7-1: A decision tree for classifying irises.
The decision tree is really an upside ...
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Finding out about decision trees