[@Gleisberg:2008ta], <span style="font-variant:small-caps;">Herwig</span> [@Bellm:2015jjp] or <span style="font-variant:small-caps;">Pythia</span> [@Sjostrand:2014zea], and the respective training samples are made known to the algorithm whether they contain signal or background respectively. However, as the Monte Carlo event samples are plagued by theoretical uncertainties, the classification algorithm will be subjected to the same uncertainties. This issue is amplified by the fact that the MVA method will usually favour highly-exclusive phase space regions which are poorly understood perturbatively [@Englert:2017aqb; @Englert:2019xhk], and often observables that are not even IR-safe are found in experimental measurements to be most discriminative, e.g. the number of charged tracks [@Schaetzel:2013vka; @Aad:2014gea], thus further questioning the reliability of theoretically predicted classification efficiencies. Adversarial neural networks have been proposed to desensitise classification methods against theoretical [@Englert:2018cfo] and systematic uncertainties [@Louppe:2016ylz] or against certain observables [@Heimel:2018mkt].

One promising approach to overcome deficits from training on pseudo-data is to train on actual data directly.[^1] While so-called data-driven methods are not subjected to theoretical uncertainties, one has to make sure that signal and background are sufficiently pure to train the algorithm on well-separated event samples. Most of the time, and in particular in searches for new physics, this is a highly challenging task. Rare processes, e.g. the production 