When to use Decision Tree Learning

• Instances are represented by attribute value pairs (can be real valued).
• The target value has discrete output values (no need to be binary, some extensions even handle real valued targets).
• Disjunctive descriptions may be required
• The training data may contain errors - errors in classifications and errors in attribute values
• Training data may contain missing attribute values.