tivity and organizational group, and the BPI 2012 dataset with 877 unique combi- nations, show a significant reduction in precision when the dimensionality is reduced from 125 to 64. Other datasets with large vocabulary show the same behaviour. Increasing the dimensionality to 500, done only for selected datasets due to the required computational effort, shows that significant overfitting occurs, especially for datasets with a large vocabulary. As shown in Fig. 7, the training precision for the BPI 2012 and BPI 2013 datasets increases significantly whereas the validation precision is not only much lower than the training precision, but decreases with increasing dimensionality. These trends are clear indications of overfitting. Examining Fig. 7 and the corresponding data in Table 5 shows that our chosen baseline with 125 dimensions is close to the optimum validation precision for all datasets and does not suffer from significant overfitting. To identify the effect of the number of unrolled steps on prediction performance, we repeat our experiments using 10 and 5 unrolled steps, keeping the other param- eters unchanged. Fig. 8 and Table 6 show the training and validation precision of selected datasets against the number of unrolled steps. Neither training nor val- idation precision is significantly affected by the number of unrolled steps. There appears to be a minor effect for the BPI 2013 Problem dataset (more pronounced when including the organizational group), where the training performance decreases with increasing number of unrolled steps, while the validation performance increases. This indicates that there are few to no long-term dependencies in the different pro- cesses that cannot be captured by even five previous steps, which is in line with the mutual information considerations in Section 3.5. 6.5. The Effect of the Number of Unrolled Steps 23