Transforming a decision tree to an equivalent rule set

Description

The process demonstrates the use of the Tree to Rules operator that transforms a decision tree to an equivalent rule based classifier. The experiment uses a decision tree built on the Zoo data set.

Input

Zoo [UCI MLR]

Output

Figure 6.6. The decision tree built on the data set.

The decision tree built on the data set.

Figure 6.7. The rule set equivalent of the decision tree.

The rule set equivalent of the decision tree.

Figure 6.8. The classification accuracy of the rule-based classifier on the data set.

The classification accuracy of the rule-based classifier on the data set.

Interpretation of the results

It is apparent in the first and second figures that each rule in the rule set corresponds to a branch of the decision tree from the root to a leaf node.

The third figure shows that the rule based classifier (and thus also the decision tree) perfectly classifies all examples.

Video

Workflow

rules_exp3.rmp

Keywords

decision tree
rule-based classifier
supervised learning
classification

Operators

Apply Model
Decision Tree
Map
Multiply
Performance (Classification)
Read AML
Subprocess
Tree to Rules