当学习器去预测其未遇到过的输入的结果时，会做一些假设（Mitchell, 1980）。而学习算法中的归纳偏置则是这些假设的集合。

机器学习试图去建造一个可以学习的算法，用来预测某个目标的结果。要达到此目的，要给于学习算法一些训练样本，样本说明输入与输出之间的预期关系。然后假设学习器在预测中逼近正确的结果，其中包括在训练中未出现的样本。既然未知状况可以是任意的结果，若没有其它额外的假设，这任务就无法解决。这种关于目标函数的必要假设就称为归纳偏置（Mitchell, 1980; desJardins and Gordon, 1995）。

The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered.

In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. To achieve this, the learning algorithm is presented some training examples that demonstrate the intended relation of input and output values. Then the learner is supposed to approximate the correct output, even for examples that have not been shown during training. Without any additional assumptions, this problem cannot be solved exactly 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.