Transduction (machine learning)
In logic, statistical inference, and supervised learning,
transduction or transductive inference is reasoning from
observed, specific (training) cases to specific (test) cases. In contrast,
induction is reasoning from observed training cases
to general rules, which are then applied to the test cases. The distinction is
most interesting in cases where the predictions of the transductive model are
not achievable by any inductive model. Note that this is caused by transductive
inference on different test sets producing mutually inconsistent predictions.
https://en.wikipedia.org/wiki/Transduction_(machine_learning)