Editing Empirical risk minimization (section)

{{Short description|Principle in statistical learning theory}}
{{Machine learning|Mathematical foundations}}
In [[statistical learning theory]], the principle of '''empirical risk minimization''' defines a family of [[machine learning|learning algorithms]] based on evaluating performance over a known and fixed dataset. The core idea is based on an application of the [[law of large numbers]]; more specifically, we cannot know exactly how well a predictive algorithm will work in practice (i.e. the "true risk") because we do not know the true distribution of the data, but we can instead [[Estimation statistics|estimate]] and optimize the performance of the algorithm on a known set of training data. The performance over the known set of training data is referred to as the "empirical risk".