The meaning of multi-class classification rules
Example: I have two classification rules (Refund is a predictor and Cheat is a binary response): (Refund, No) → (Cheat, No) Support = 0.4, Confidence = 0.57 (Refund, No) → (Cheat, Yes) Support = 0.3, Confidence = 0.43
=> multi-class classification rules:
(Refund, No) → (Cheat, No) v (Cheat, Yes)
When predicted classification for test data, (Cheat, No) will be selected priority so why we need to have (Cheat, Yes) in multi-class classification rules here?