In logic, statistical inference, and supervised learning,transduction or transductive inference is reasoning fromobserved, specific (training) cases to specific (test) cases. In contrast,induction is reasoning from observed training casesto general rules, which are then applied to the test cases. The distinction ismost interesting in cases where the predictions of the transductive model arenot achievable by any inductive model. Note that this is caused by transductiveinference on different test sets producing mutually inconsistent predictions.