## 3 Dataset 1: Austrian Federal Forests
### Predictive Models
#### 3.3.2 Classification  
Dealing with the challenge of classifying fatal and non-fatal cases was complex due to a significant class imbalance, where only nine fatal cases were present among 1965 data samples. This imbalance often leads to classifiers achieving a high accuracy but yielding very low, almost negligible mutual information (MI), as discussed in [34]. Therefore, for classification purposes, metrics such as the confusion matrix and mutual information (MI) [33] were utilized. To address the imbalance issue, oversampling was performed on the minority class. Leveraging the Synthetic Minority Oversampling Technique (SMOTE) [41] with the assistance of the imbalanced-learn Python package [42] facilitated the creation of a balanced dataset. Employing four as the number of neighbors to generate synthetic samples yielded the most promising performance outcomes.  
_Decision Tree_  
Following a method similar to that outlined in the regression section (refer to Section 3.3.1), the decision tree parameters were determined through a grid search. For the balanced dataset after oversampling, using four features, we observed a mutual information (MI) of 0.60, achieving an accuracy rate of 0.98. The confusion matrix showed the following values: true negatives: 463, true positives: 470, false positives: 17, and false negatives: 2 [4]. Notably, the feature \"worker or employee\" emerged as the most significant in terms of importance despite being binary. This suggested its substantial discriminatory power, even compared to other categorical or continuous features. It is worth mentioning that among the raw data, five out of nine fatal accidents occurred among employees. Textual representations of the decision tree's rules, similar to those derived for the regression task in Section 3.3.1, can be presented.  
Transitioning to the dataset featuring seven input features, the mutual information (MI) increased to 0.67, accompanied by an accuracy rate of 0.99. Figure 6 visually depicts the feature importance values.  
_Random Forest_  
For the random forest model with 100 estimators and four input features, the mutual information (MI) was 0.63, with an accuracy of 0.98. With seven input features, the MI increased to 0.69, and the accuracy reached 1.0 [4]. Moreover, the confusion matrix showed no false positives or negatives, indicating a \"perfect\" classification outcome. However, it is essential to approach these results with caution as they may suggest overfitting, necessitating further examination.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Decision Tree** & **Random Forest** & **Neural Network** \\ \hline MSE (a) & 151,222.99 & 121,154.73 & 93,215.32 \\ RRMSE (a) & 1.15 & 1.84 & 35.39 \\ MSE (b) & 164,610.79 & 114,203.96 & 155,141.41 \\ RRMSE (b) & 1.83 & 1.71 & 37.97 \\ \hline \end{tabular}
\end{table}
Table 1: Comparing the performance of different models with 4 (a) and 7 (b) features; reprinted with permission from [4], [PERSON] 2022.  
Figure 5: The importance of features in the neural network trained with 4 features; reprinted with permission from [4]. [PERSON] 2022.  
_Fully Connected Neural Network_  
In the classification task (refer also to Table 2), the neural network architecture matched the one utilized for the regression, with a notable alteration: the use of a sigmoid non-linearity in the output layer. When considering the dataset with four features, the mutual information (MI) was measured at 0.64, alongside an accuracy rate of 0.98. Upon expanding to seven features, the MI improved to 0.67, while the accuracy further improved to 0.99 [4].