# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 5 Results and Observations
### Global Methods Comparability
#### 5.1.2 Execution Times  
For applied XAI methods, the execution times are computed for SHAP, ALE and PFI over the training event logs. Prediction times of LR and XGBoost are included as the time for computing the importance of the features to the predictive models. Execution times are presented in Tables 3 and 4. More precisely, the execution times of event logs preprocessed with single aggregation and prefix index combination respectively are displayed. Note that in the event log column in Table 4, each event log is augmented with the length of its prefixes.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Event Log** & \multicolumn{4}{c}{**XGBoost**} & \multicolumn{4}{c}{**Logistic Regression (LR)**} \\
**Prediction** & **SHAP** & **ALE** & **PFI** & **Prediction** & **SHAP  
In terms of the underlying preprocessing technique, the overall numbers across all XAI methods show that event logs preprocessed with prefix index combination are faster to be explained than those preprocessed with single aggregation.  
**Observation 7:** We conclude that the chosen bucketing technique is a determinant factor affecting execution duration of global explanations generation.  
As illustrated by [2; 3], in index encoding, with increasing prefix length, the number of features increases, while the number of process instances decreases in an event log. The reduced number of process instances in a prefix index-preprocessed event log compared to a single-aggregated event log can justify the faster execution times on the former logs. Despite enabling faster executions, as expected, prefix-indexed event logs with longer prefixes show longer execution times over all XAI methods than event logs with shorter prefixes.  
**Observation 8:** The overall numbers show that explaining decisions of a LR model takes less time compared to the use of an XGBoost-based model.  
This observation has provedn to be true when regarding the results across different event log sizes and preprocessing settings as well as over different XAI methods. A justification of this observation may be that all studied global XAI methods involve querying the underlying model. The boosting mechanism employed in XGBoost complicates the prediction process, and hence it demands more time to produce predictions or, subsequently, explanations.  
**Observation 9:** When regarding execution times of different XAI methods, it can be observed that SHAP has the fastest executions over almost all event logs, regardless the used preprocessing techniques (with few exceptions).  
An exception to this observation is the execution time of SHAP over the shortest prefix version of _Traffic_fines_ event log being preprocessed with prefix index combination and having an XGBoost model trained over its process instances. SHAP values for this event log are the slowest among all XAI methods.  
**Observation 10:** The slowest XAI method differs based on the preprocessing techniques used, with ALE performing worst in most cases.  
In prefix-indexed event logs, PFI achieves the worst performance for most event logs when combining it with LR, and ALE shown the worst performance in combination with XGBoost. In turn, in single aggregation-based event logs, ALE provides the least efficient XAI method to be combined with either predictive models. PFI performs worst in smaller event logs with relevant small numbers of features compared to other logs, e.g., in the three _Sepsis_ event logs. Figures 13 and 14 in Appendix A provide a visualisation of the execution times of XAI methods on event logs using prefix index and single aggregation preprocessing combinations, respectively.