# XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework
## 5 Results and Observations
### Local Methods Comparability  
LIME and SHAP, which are the two local XAI methods used in this study, share the underlying mechanism. Both are feature additive attribution methods [5]. LIME is looking for the weights of features in order to indicate their importance, While SHAP calculates contributions of features to shifting the current process instance prediction towards or apart from a base prediction. To gain insights into how LIME and SHAP results can differ while explaining the same process instances, we compared their explanations of both predictive models reasoning, i.e., LR and XGBoost, over a selected set of process instances from each event log preprocessed with both preprocessing combinations. After analysing the resulting explanations, the following observations can be made:  
**Observation 11:**_It could be rarely observed when both methods match in their attributions to important features or in the strength and direction of the effect features have on driving the current prediction towards or away from the base prediction._  
It is supposed that whenever both XAI methods explain the prediction generated by the same predictive model for the same process instance, a similarity between the explanations exists. However, the former observation is valid across all event logs regardless the preprocessing combinations used. If both methods agree on a subset of features, they do not have the same importance ranks. Note that in LIME the goal is to identify a set of features which are important in confirming the current prediction or driving the prediction towards the other class in case of binary classification tasks. In turn in SHAP the purpose is to order features descending based on their SHAP values, i.e., their contributions in driving the current prediction away or towards the base prediction over the whole event log. As example consider the two explanations from Figure 12, which shows the LIME explanation (cf. Figure 11(a)) and the SHAP decision plot (cf. Figure 11(b)) of prediction made by LR of the same process instance of _Traffic_fines_ event log preprocessed with prefix index. In Figure 11(a), important features are listed in descending order along with their values in the event log based on the coefficients of these features in the approximating model created by LIME. On the bottom of Figure 11(a), again these features are listed with the percentage of their contributions. Note that the color code of these features represents whether a feature is driving the prediction towards the currently predicted class or towards the other class. In this case, the currently predicted class is \"deviant\".  
Figure 11(b) shows the decision plot highlighting the features with the highest shap values in descending order. The line at the center represents the base prediction or model output. The zigzag line represents the current prediction and sticks the top at exactly the current prediction value. Prediction units are in log odds. The pattern at which the zigzag line is going towards and outwards from the line at the center represents contributions of features at the y-axis to reducing/increasing the difference between the base and current predictions. In addition, feature values are stated in brackets over the zigzag line at the point where the line intersects with the horizontal line. It can be observed that the decision plot is indicating that the current prediction of the LR model is highly affected by collinear features.  
Figure 12: LIME vs. SHAP explanations of LR prediction for one instance in _Traffic_fines_ preprocessed with prefix index combination.