# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 5 Results
### Effect of the applied preprocessing configurations
#### 5.2.2 Observations of Global XAI Methods Results
Figure 11: PFI according to XGBoost trained over _BPIC2017_Refused_ (preprocessed with prefix index configuration). In terms of explaining the predictions of the LR model, performing two executions of the SHAP method results neither in different feature sets nor in different ranks based on SHAP values, regardless of the preprocessing configuration used. In contrast, explaining the predictions of XGBoost model reveals that having the highest-contributing feature is the same across both execution runs. Furthermore, the rest of the feature set happens to be the same, with different respective ranks. An exception is present in the feature set of the two _Sepsis_ event logs, where feature ranks are the same across both runs. These observations are aligned with the observations made regarding model-specific explanations and feature importance according to XGBoost-based models across two execution runs. In order to study how SHAP results relate to feature importance as revealed by the predictive models, it becomes necessary to compare the features ranked according to their SHAP values with the important feature sets as indicated by the predictive models used in these experiments. **Observation 16.1**: _Important features according to their ranking based on their LR coefficients are unaligned with important features ranked by their SHAP values._  
This observation is valid for all considered event logs regardless of the preprocessing configuration. **Observation 16.2**: _When using the total gain as a criterion to rank important features according to XGBoost models, the former observation is not valid._  
In prefix-indexed event logs, the two compared feature sets are somehow similar. In contrast, in single-aggregated event logs, only in _BPIC2017_Accepted_ and _Sepsis1_ does there exist any similarity (though not a complete match) between the two compared feature sets. **Observation 17**: _Despite adjusting SHAP explainer parameters to be true to the model rather than the underlying relations between features, SHAP results reveal high multicollinearity between the most important features_  
This observation especially holds for predictions based on the event logs preprocessed with the single-aggregation configuration. It is valid for the feature sets that are important to LR models, and is valid for the XGBoost models in the case of the _Sepsis1_ event log as well. Multicollinearity in the underlying data affects LR, as its presence counteracts the underlying assumptions of LR. In contrast, multicollinearity is not supposed to affect XGBoost. This claim is justified by the fact that, in boosting, whenever collinearity exists between a subset of features the model chooses one feature to be the data splitting criterion. The entire importance score is assigned to the splitting feature, in contrast to the excluded correlated features, which are not considered important in this case [29]. However, in case of the _Sepsis1_ event logs, the very high class imbalance (cf. Table 4) indicates that the model overfits patterns in training data. Moreover, in event logs preprocessed with the prefix-index configuration, high correlations can be observed between features in the feature sets ranked highly according to LR with increasing prefix length. Multicollinearity in single-aggregated event logs is mainly inherited from high correlations present in original event logs. This multicollinearity increases with single aggregation preprocessing of an event log. According to SHAP values, for certain event logs LR models depend on only one feature. The aforementioned logs are event logs with a prefix length of one, namely, those extracted from the event logs _BPIC2017_Refused_, _Sepsis1_, and _Sepsis2_. SHAP nullifies the contribution of features when they are not important to a predictive model. Therefore, for these event logs, only the important feature has a contribution value, while all other features receive zeros.