Active class selection provides machine learning practitioners with the freedom to actively choose the class proportions of their training data. While this freedom can improve the model performance and decrease the data acquisition cost, it also puts the practical value of the trained model into question: is this model really appropriate for the class proportions that are handled during deployment? What if the deployment class proportions are uncertain or change over time? We address these questions by certifying supervised models that are trained through active class selection. Specifically, our certificate declares a set of class proportions for which the certified model induces a training-to-deployment gap that is small with a high probability. This declaration is theoretically justified by PAC bounds. We apply our proposed certification method in astro-particle physics, where a simulation generates telescope recordings from actively chosen particle classes.