\section{Performance Validation}

When applying a model to a real-world problem, one usually wants to
rely on a statistically significant estimation of its
performance. There are several ways to measure this performance
by comparing predicted label and true label. This can of course
only be done if the latter is known. The usual way to estimate
performance is therefore, to split the labelled dataset into a
training set and a test set, which can be used for performance
estimation. The operators in this section realise different ways of
evaluating the performance of a model and splitting the dataset into
training and test set.
