## 3 Dataset 1: Austrian Federal Forests
### Predictive Models  
#### 3.3.1 Regression Task  
Decision Tree  
In the context of non-fatal accidents, the primary predictive objective is to anticipate the duration of recovery for the injured individual, which varies between workers and employees. This distinction arises from the distinct nature of the recovery process for each group, influenced by the nature of their respective tasks. As depicted in Figure 1, the distributions of recovery duration for workers and employees exhibit noticeable differences, aligning with expectations due to the higher incidence of severe accidents in manual labor settings. The used approach involved developing a unified model capable of discerning both shared patterns and unique characteristics between these two groups. However, it is important to remain cognizant of the potential need to construct separate, more distinct models tailored to each group's specific attributes in future iterations. This problem constitutes a regression task [37], and employing a decision tree (DT) [33] is a straightforward and inherently interpretable approach for such a task. Given that the numeric or continuous features did not require scaling, attention shifted to non-ordinal input features such as injured body parts, cause of the accident, and the working sector. Some features already possessed integer mappings, but in most instances, these mappings lacked an ordinal relationship, so it was necessary to prevent the predictive model from assuming such a relationship in its decision-making or generalization. Moreover, as mentioned in the preceding Section 3.2, a grouping of conceptually similar values was applied to these features, consolidating them into single categories. To encode these categories, one-hot encoding was employed [36], where each category is represented by a vector with only one entry orthogonal to the other. However, this approach introduces challenges; the resulting feature values matrix tends to be sparse and large due to the presence of a column for each category. The analysis of the decision tree (see Figure 2) revealed that younger employees and workers were anticipated to require a relatively short recovery period, depending upon the time of the accident. Additionally, distinctions emerged between accidents occurring during the start of the week and those happening later in the week. Figure 1: Working hours lost for workers (blue-collar workers) and employees (white-collar workers), reprinted with permission from [4], [PERSON] 2022. The dataset underwent shuffling and division into two sets: 80% designated as the training set and 20% for the test set. A grid search was conducted on the training set to determine the optimal parameters for the decision tree. These parameters were:  
* Maximum depth: five
* Maximum number of features to consider: \"auto\" (suggesting that the best split was determined by considering all available input features)
* Minimum number of samples required for a split: two  
After evaluation on the test set utilizing four features--specifically, the individual's age at the time of the accident, the day of the week, the time of day, and the classification as a worker or an employee--the decision tree showed a Mean Squared Error (MSE) of 151,222.99 and a Relative Root Mean Squared Error (RRMSE) of 1.15 [4]. The significance of these input features is consistent with previous research findings [7,19], as illustrated in Figure 3.  
Figure 3: The importance of features in the decision tree trained with 4 features; reprinted with permission from [4], Springer 2022. Figure 2: The acquired rules of the decision tree, which was trained using 4 features; reprinted with permission from [4], Springer 2022. Training with all seven input features indeed affected the performance metrics, resulting in an MSE of 164,610.79 and an RRMSE of 1.83. Additionally, the learned rules of the decision tree became more detailed, leading to a change in the order of importance of features.