## 3 Methodology
### Data Standardization
#### 3.3.3 Feature Selection  
Following the data curation that was described in the preprocessing section, the methodology continues with the process that most of the data mining and ML guides define as feature selection. The advantages of reducing the features to a subset of them are well described in the literature [40], and affects many aspects of a ML experiment, such as the speed of training, the accuracy and the explainability of a model. Feature selection algorithms, based on their output, can be categorized into two different categories. The first category is feature weighting which returns the same number of input features along with their weights by employing wrapper feature selection algorithms. The second category is subset selection which returns a subset of the input features by employing either a filter or embedded model feature selection algorithms. Our methodology involved the selection of two feature subsets based on two different strategies. The first strategy primarily aims at creating a subset of features in which at least some of the in-game related features will be included. The mandatory inclusion of some of these features is related to the research question of this work, since it would have been pointless to train a model based only on data from the questionnaires. The second strategy used the method of feature selection with the _chi2_ statistic as the scorer function, a method that eliminates features with low correlation to the target class. Feature Correlation Inspection  
At first, the pairwise correlation between each feature is inspected. For this task, [PERSON]'s correlation was calculated and projected on the heatmap shown in Figure 4. The purpose at this stage is to recognize the highly correlated features and eliminate the so-called redundant features, which are those that cannot append additional information to the model [41]. To avoid the daunting task of manually using the heatmap to find the highly correlated features, a function that performs agglomerative clustering, was used, resulting in feature clusters separated based on the degree of their correlation which were previously calculated [42]. The dendrogram in Figure 5 projects the clusters that are formed based on a threshold value of 36% that was empirically selected and represents the maximum pairwise distance observed which in this case happens to be 4.68. Feature Importance Inspection  
Having every feature grouped into clusters of highly correlated features, the next step of the methodology is to inspect their significance against the target class, with the ultimate goal of keeping only the most important one of each cluster. To decide whether a feature is important or not two metrics were incorporated, the mean decrease in impurity (MDI) and the mean decrease in accuracy (MDA), also known as permutation importance. Essentially, this is a form of feature weighting, thus a wrapper method is needed in order to calculate these metrics. The wrapper method that was implemented incorporates a Random Forest classifier that is used as an estimator both for the MDI and the MDA metrics. The wrapper  
Figure 4: Heatmap of pairwise correlation of features calculated with Pearson’s correlation. method was then called once for the complete set of features, excluding those already removed in the preprocessing, and then once for each cluster separately (Figure 6a,b). To proceed with the custom selection process, judging by the MDA and MDI scores, the features that appear to perform worse than the two randomized features were excluded, followed by the exclusion of the less important features of each cluster. The features that remained after the low variance feature removal, were inspected for their pairwise correlation and for their importance against the target class in order to create an optimized feature subset. This subset is identified next as the manually selected features.