Concept
Inductive bias
Inductive bias is the set of assumptions a model uses to generalize from finite training data to inputs it has never seen. Without such assumptions, any function consistent with the training set is equally valid, and the model has no reason to prefer one over another.
The rest of “Inductive bias” is a premium feature: every concept in the library gets a precise, practitioner-focused write-up like this one, cross-linked straight from the paper summaries that use it.
Log in to unlock→