APPROPRIATE PROBLEMS FOR DECISION TREE LEARNING

Decision tree learning is generally best suited to problems with the following characteristics:

1. Instances are represented by attribute-value pairs – Instances are described by a fixed set of attributes and their values
2. The target function has discrete output values – The decision tree assigns a Boolean classification (e.g., yes or no) to each example. Decision tree methods easily extend to learning functions with more than two possible output values.
3. Disjunctive descriptions may be required
4. The training data may contain errors – Decision tree learning methods are robust to errors, both errors in classifications of the training examples and errors in the attribute values that describe these examples.
5. The training data may contain missing attribute values – Decision tree methods can be used even when some training examples have unknown values