# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 6 Discussion  
Explaining ML-based predictions is a prerequisite for gaining user acceptance and trust in the predictions of respective models. It becomes necessary to consider explainability as a continuous process that needs to be integrated throughout the ML pipeline. A first step towards such integration would be to study the effects of different pipeline decisions on the resulting explanations. The main concern in our research is to study the ability of an explanation to reflect how a predictive model is affected by different settings in the ML pipeline. Another concern is to study how the inherent characteristics of a predictive model are confirmed and highlighted through an explanation. Our experimental results, described and analysed in Section 5, confirm the following conclusions:  
* Encoding techniques used in the preprocessing phase of PPM have a major impact on both the ability of the predictive model to be decisive as to what the important features are and the selection of certain XAI methods to explain the model's predictions. Both studied encoding techniques load the event log with a large number of derived features. However, the situation is worse in index-based encoding, as the number of resulting features increases proportionally with the number of dynamic attributes, especially the number of categorical levels of a dynamic categorical attribute. In aggregation-based encoding, the number of resulting features increases in proportion with the number of dynamic attributes. Figure 12: Summary plots of SHAP values of features used by LR. Increased collinearity in the underlying data constitutes another problem, resulting from encoding techniques in varying degrees. The effect of collinearity is observed in index-based preprocessed event logs, and is not totally absent in aggregation-based event logs. This collinearity is reflected in explanations of predictions with respect to process instances from prefix-indexed event logs as the length of a prefix increases. * The selected bucketing technique has an effect on global XAI methods, especially when accuracy depends on the sufficiency of the process instances to be analysed. PFI is affected by the number of process instances as the average of errors in prediction is calculated over the number of event log process instances. * Our experiments show the sensitivity of LR to collinearity in several situations. This conclusion can be made, for example, when comparing features ranked highly based on SHAP and PFI to high-importance features based on LR coefficients. In contrast, there is a degree of similarity when comparing the former important feature sets to XGBoost important features sets, especially for explanations of predictions from prefix-index preprocessed event logs. However, when querying the XGBoost model for the set of important features twice, the resulting sets do not match. Such a mismatch indicates inconsistency as a result of the collinearity between features. Note that both predictive models are affected by collinearity. However, in LR the effect is magnified, and prevails in all comparisons in which LR coefficients take part. In most cases, it is observed that dimensionality and collinearity prevent both LR and XGBoost from relying on features that have a dependency relationship with the label. This discussion emphasizes the importance of considering the explanation of an event log as an accumulated effort, starting from features analysis and selection stage, to training a predictive model, and finally to an explanation model. Despite having XAI methods true to the model, explanations have the power to highlight how underlying data characteristics affect and are reflected in the model's reasoning process. The experiments executed in the context of this study are open to improvements and enhancements. For example, more real-life event logs are available; experimenting on more event logs with more varied characteristics may enable different observations to be obtained after the preprocessing step.