Empirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on their performance. The core idea is that we cannot know exactly how well an algorithm will work in practice (the true "risk") because we don't know the true distribution of data that the algorithm will work on, but we can instead measure its performance on a known set of training data (the "empirical" risk). Consider the following situation, which is a general setting of many supervised learning problems. We have two spaces of objects and and would like to learn a function (often called hypothesis) which outputs an object , given . To do so, we have at our disposal a training set of examples where is an input and is the corresponding response that we wish to get from . To put it more formally, we assume that there is a joint probability distribution over and , and that the training set consists of instances drawn i.i.d. from . Note that the assumption of a joint probability distribution allows us to model uncertainty in predictions (e.g. from noise in data) because is not a deterministic function of , but rather a random variable with conditional distribution for a fixed . We also assume that we are given a non-negative real-valued loss function which measures how different the prediction of a hypothesis is from the true outcome The risk associated with hypothesis is then defined as the expectation of the loss function: A loss function commonly used in theory is the 0-1 loss function: . The ultimate goal of a learning algorithm is to find a hypothesis among a fixed class of functions for which the risk is minimal: For classification problems, the Bayes classifier is defined to be the classifier minimizing the risk defined with the 0–1 loss function. In general, the risk cannot be computed because the distribution is unknown to the learning algorithm (this situation is referred to as agnostic learning).