Figure 8: Validation precision for three values of unrolled steps for selected datasets Table 6: Validation precision for different number of unrolled steps 6.6. Interpreting the RNN One of the main criticisms of neural networks is the fact that no explicit, human understandable model is created; the learning consists entirely in the optimization of the values of thousands or millions of floating point parameters. We present two ways in which users can gain insight into what the neural net has learned, i.e. the knowledge that is encoded in its structures. 24 Number of unrolling steps Tr aining Precision 0.5 0.6 0.7 0.8 5 10 20 BPI 2012 (completion events) BPI 2012 (completion events, resource) BPI 2013 Incidents BPI 2013 Incidents (group) BPI 2013 Problems BPI 2013 Problems (group) Number of unrolling steps V alidation Precision 0.5 0.6 0.7 0.8 5 10 20 Dataset No Resource Info With Resource Info Unrolled Steps Unrolled Steps 20 10 5 20 10 5 BPI2013.Incidents .735 .730 .731 .595 .595 .600 BPI2013.Problems .628 .612 .612 .517 .490 .478 BPI2012 (completion events) .788 .787 .787 .594 .596 .596 BPI2012 (all events) .859 .860 .860 .663 .667 .667 BPI2012.W (completion events) .658 .657 .657 .208 .201 .201 BPI2012.W (all events) .832 .831 .831 .559 .558 .558 BPI2012.A .832 .833 .834 .709 .710 .712 BPI2012.O .836 .836 .836 .590 .590 .590