Inductive Bias

Inductive bias refers to the set of assumptions a machine learning algorithm uses to generalize from specific training data to broader situations. Since computers learn patterns from limited examples, inductive bias guides them to make predictions about new, unseen data. For example, assuming that nearby points are likely related or that trends continue helps the model infer meaningful rules. Without such biases, learning would be impossible, as data alone doesn’t provide enough information. Essentially, inductive bias shapes the way algorithms generalize, influencing their accuracy and ability to make reliable predictions beyond their training set.