# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 4 Experiments
### Experimental Setup
#### 4.1.2 Dimension 2: Preprocessing  
As discussed in [2; 12], the chosen bucketing technique might have an effect on the perceived performance of the predictive models. The more information available to the predictive model at the time the prediction is generated, the more accurate the prediction. Bucketing techniques can affect the information available to a predictive model if the bucketing prefixes are in the same execution state, have the same length, or if all prefixes from all traces are bucketed together. Here, we want to study whether the influence of a bucketing technique can be propagated and become clearer in explanations generated for a given prediction. Moreover, the encoding technique equips the predictive model with different sets of features. As a result, its predictions are influenced by the characteristics of the encoding techniques. Examples of encoding technique characteristics include information loss or increased dimensionality [2]. The obligation of using either shorter traces or traces with gaps results from the latter characteristic. In order to bucket traces of a chosen event log, we applied prefix-length bucketing with a gap of five events in addition to single bucketing. We believe that this gap size may enable a balanced situation to be reached in which only few prefixes are produced in case of event logs with longer traces, while enough prefixes remain available in case of event logs with shorter traces. Furthermore, the choice of a moderate gap size prevents a large number of events from being left out and avoids the risk of information loss, unlike in the case of larger gap sizes. Finally, this choice avoids overloading the experiments with large numbers of buckets when using a prefix size-dependent bucketing technique or single bucketing technique, unlike in the case of smaller gap sizes. Moreover, we applied aggregation and index-based encoding techniques. Usually, both techniques are coupled with static encoding in order to transform static attributesfor use as input to the predictive model along with their dynamic counterparts. Two configurations of bucketing and encoding techniques were applied to satisfy both pre-processing tasks. These configurations were _single-aggregation_ and _prefix-index_. As index encoding leads to a dimensionality explosion (because each categorical level of each feature is encoded as a separate column), we decided to apply this encoding only to the following event logs: _Sepsis_ (i.e., the two derived event logs), _Traffic_fines_, and _BPIC2017_Refused_. Our decision to choose these two configurations, i.e., single-aggregation and prefix-index, was driven by several factors. * Considering that certain encoding techniques turn subcategories of categorical attributes into separate features, we wanted to study how the relationship between the resulting new features is reflected though an explanation. * We needed to study the ability of explanations in order to reflect differences in the reasoning process of a predictive model when all information is available in one input bucket. We aimed to compare the former situation against others when several predictive models are trained based on different groups of traces that differ in their lengths, and hence in the amount of information provided to a single predictive model. * Our goal was to ensure the coherence of each scenario when a PPM pipeline is supported with explanations on top of it. By coherence, we mean that a scenario employs the best-performing techniques so as not to result in explanations which might be affected by limitations of the underlying choices. For example, we excluded the _last-state_ encoding technique from our experiments, as its working mechanism ends with a scenario in which the predictive model is provided with limited information that is often insufficient for making accurate predictions [2].