# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 5 Results
### Effect of the applied preprocessing configurations  
When running LR over the used event logs twice, the following observations were made. **Observation 6:**_Event logs preprocessed with the single-aggregation configuration show a 100% match in features coefficient analysis with respect to the features set and importance scores. However, the event logs do not show any similarity between the most important features, as indicated after querying the model and the features with high dependency on the label, according to MutInfo analysis._  
The _Traffic_fines_ event log represents an exception to the latter observation. Analysis of the results of this event log show a similarity between the first two features with the highest LR coefficients and the first two features with high dependency on the label, according to MutInfo analysis. High collinearity between these two features is observed with respect to correlations analysis, as they constitute aggregations of \"timesincelastevent\" feature. **Observation 7:**_Event logs preprocessed with the prefix-index configuration show similarities in feature coefficient analysis in terms of the features set with increasing prefix length. However, the importance scores remain different across several execution runs._  
Moreover, the similarity between the most important features according to the model and the set of features with high dependency on the label increases as the prefix length  
Figure 6: Collinearity between categorical attributes in _Sepsis2_. increases. An exception to both observations with respect to prefix-indexed event logs is provided by the _Traffic_fines_ event log. Here, the feature sets are not similar either when comparing them across the two runs of LR model coefficients analysis or when comparing them to the indicated features with high MutInfo values. As indicated in Table 4, _Traffic_fines_ provides only shorter prefixes. Therefore, dissimilarity between feature sets across execution runs on this event log could be a result of its short prefixes. As a consequence, it is not possible to observe a change in longer traces, unlike in other event logs. **XGBoost characteristics revealed when using the applied preprocessing configurations**  
Referring back to the criteria provided by XGBoost to order features according to their importance, _gain_ is considered to be the most relevant attribute in measuring the relative importance of each feature. We decided to rely on _gain_ importance as provided by XGBoost, as it can be complementary to MutInfo, that is, the measurement representing the expected gain from a data analysis perspective. Comparing results from both a data analysis perspective and a model reasoning perspective, as well as observing how both sides (dis)agree, may lead to interesting insights which can highlight how a predictive model learns from the underlying data and reflects any anticipated characteristics. However, in our experiments we compare feature importance ranks based on all five criteria available in order to rank the importance of XGBoost features. These criteria are presented in Section 4.1.3. Note that in our observations there are two usages of the term _weight_, i.e., _weight_, indicating one of the five feature importance criteria analysed, and _the weight_ indicating the importance score according to one of the five importance criteria. The following observations are made as a result of analysing features importance according to XGBoost-based models. _Observation 8: XGBoost models show complete inconsistency across the two execution runs for all event logs preprocessed with the single-aggregation configuration._  
The two _Sepsis_ event logs constitute an exception. They have the same feature set with different importance scores for four of the five criteria, whereas the feature set increasing the gain of the model is totally different across the two execution runs.