Abstract: A method for predicting a suitable configuration of a machine learning system for a first training data set. The method starts by training a plurality of machine learning systems on the first training data set, the machine learning systems and/or the training methods used being configured differently. This is followed by a creation of a second training data set including ascertained performances of the trained machine learning systems and the assigned configuration of the particular machine learning systems and/or training methods. This is followed by a training of a graph isomorphism network, depending on the second training data set, and a prediction in each case of the performance of a plurality of configurations not used for the training, with the aid of the GIN. A computer program and a device for carrying out the method and a machine-readable memory element, on which the computer program is stored, are also described.