Decision tree learning is generally best suited to problems with the following characteristics:
- Instances are represented by attribute-value pairs – Instances are described by a fixed set of attributes and their values
- 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.
- Disjunctive descriptions may be required
- 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.
- The training data may contain missing attribute values – Decision tree methods can be used even when some training examples have unknown values
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