# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 4 Experiments
### Experimental Setup
#### 4.1.1 Dimension 1: Data
Those not meeting this requirement were filtered out and replaced with the word \"other\" in the event log. The analyses related to this dimension are intended to pinpoint the data characteristics and relations that may affect the patterns learned by a predictive model. Types of analysis in this dimension include:  
* _Correlations analysis._ Correlation coefficient constitutes a measurement used to describe the degree to which two variables are linearly related [23]. It takes values between \(-1\) and \(1\), with higher values indicating a stronger relationship. The sign  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline
**Event log** & **Fileness** & **Shen** & **Abs. ** & **Long. ** & **Long. ** & **Max. ** & **Cour. ** & **Spen. ** & **Agent** & **Agent** & **Agent** & **Agent** & **Agent** & **Agent** & **Agent** & **Agent** & **Agent** \\  & **Tracerepresents the direction of the relation. Correlation coefficient is computed to allow the normalised values of features to be investigated. For original event logs, we computed the correlations between categorical attributes before encoding them with Cramer's V coefficient [24]. The latter measures the correlation between two nominal variables and is based on Pearson's chi-square. * _Mutual Information (MutInfo)_. Correlations between features indicate the degree to which those features are dependent. However, correlations are not decisive with respect to the independence of features [23]. This means that if two variables are independent, their correlation coefficient equals zero. However, note that a correlation coefficient of zero does not indicate the independence of two variables [23]. We measured the MutInfo of features with respect to the label. MutInfo is the reduction of uncertainty in a variable after observing the dependent one [23]. This analysis is capable of capturing any kind of dependency, unlike the F-test, which captures only linear dependency [25]. Results take values between 0 and 1, with 0 meaning that the predicted label is independent of the feature and 1 meaning that both are totally dependent. * _Profiling an event log_. For each event log, we generated a statistical profile called the _pandas profile_[26] both before and after preprocessing. Each pandas profile reports on statistical characteristics of each feature within the event log. Such characteristics include, for example, descriptive statistics of the feature, quantile statistics, missing values, most frequent values, and histograms. Experiments along the data dimension were applied to the original event logs before starting the preprocessing step of the PPM workflow, i.e., before bucketing and encoding an event log. Afterwards, the same experiments were repeated on the transformed event logs. In this way, we analysed the effect of the increased dimensionality of an event log on the relationships between latent features that result from feature encoding. In addition, in the case of categorical attributes, experiments were performed in order to study which levels were highly correlated to other attributes.