Explaining the numbers in a decision tree

Question

Using the famous Iris data set with Julia decision tree classifier I get the following tree.

using RDatasets
using DecisionTree
iris = dataset("datasets", "iris")
features = convert(Array, iris[:, 1:4])
labels = convert(Array, iris[:, 5]);
model = build_tree(labels, features)
model = prune_tree(model, 0.9)

print_tree(model)
Feature 3, Threshold 3.0
L-> setosa : 50/50
R-> Feature 4, Threshold 1.8
    L-> Feature 3, Threshold 5.0
        L-> versicolor : 47/48
        R-> Feature 4, Threshold 1.6
            L-> virginica : 3/3
            R-> Feature 1, Threshold 7.2
                L-> versicolor : 2/2
                R-> virginica : 1/1
    R-> Feature 3, Threshold 4.9
        L-> Feature 1, Threshold 6.0
            L-> versicolor : 1/1
            R-> virginica : 2/2
        R-> virginica : 43/43

I can't really interpret the numbers after some of the branches, like "setosa : 50/50" or "virginica : 3/3".

Could somebody explain what those mean?

score 5 · Accepted Answer · answered Apr 30 '16 at 08:30

The labels give the number of observations with the predicted majority class and the overall number of observations in that node.

Thus, "versicolor : 47/48" means that in the corresponding node there are 48 observations out of which 47 are versicolor. Consequently, one observation (of class virginica) is misclassified in that node.

Explaining the numbers in a decision tree

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