Inductive bias

[2] An inductive bias allows a learning algorithm to prioritize one solution (or interpretation) over another, independently of the observed data.

To achieve this, the learning algorithm is presented some training examples that demonstrate the intended relation of input and output values.

Without any additional assumptions, this problem cannot be solved since unseen situations might have an arbitrary output value.

The kind of necessary assumptions about the nature of the target function are subsumed in the phrase inductive bias.

[1][4] A classical example of an inductive bias is Occam's razor, assuming that the simplest consistent hypothesis about the target function is actually the best.