intentionally imperfect representations between raw event logs and event prediction. We chose RNNs for their natural fit to the sequence data of process event logs. An alternative to RNN are n-gram models, in which a non-recurrent neural network is trained on all fixed-length trace prefixes of length n . The RNN approach has the advantage of allowing prediction from trace prefixes of arbitrary length while also offering better prediction performance (Mikolov et al., 2011). Alternatives to neural networks are probabilistic automatons such as HMM (Lakshmanan et al., 2015; Unuvar et al., 2016) and PFA (Breuker et al., 2016). Section 3.5 showed that the event logs chosen for this study have characteristics that make either of these techniques applicable. Our prediction results confirm this. While our RNN are not always superior, they surpass the performance of PFA in many cases. However, other processes may involve long-distance dependencies between events that are not found in our data. Such long-distance dependencies in an event log are likely to present problems for HMMs and n-gram models, but not for RNN (Lin and Tegmark, 2016). Deep learning approaches are susceptible to overfitting. The larger a neural network, the better it can capitalize on chance training data idiosyncrasies. With an embedding space of 500 dimensions and a small event log, Evermann et al. (2016) report training data prediction precision in excess of 99%, showing clear overfitting. In this paper, we have also seen overfitting with similarly large embedding spaces. In the context of process model discovery, overfitting has its analogue in mined models with high fitness and precision, that are complex and not generalizable. And similar to quality trade-offs in process model mining, deep learning also makes trade- offs. Prediction precision is traded off against generalizability (by overfitting) and simplicity (network size, embedding dimensionality). Just as model fitness must be evaluated in the context of other quality dimensions, predictive performance must be evaluated in the context of generalizability and model size. An assessment of 30